Miracle-dz/UniFAR-FTUs huggingface dataset:
Seed Evaluation using Infrared Thermology

----------------------------------Abstract: Availability of high-quality seeds is essential for agriculture, horticulture as well as seedling productivity, since the seed quality is tightly linked to the resistance to biotic and abiotic stress, to the germination rate and to the plant performance. Thermal cameras have historically been of interest mainly for military applications. Increasing image quality and resolution combined with decreasing price and size during recent years have, however, opened up new application areas. The most significant area of research is the agriculture. Thermal cameras are useful as soon as it is possible to measure a temperature difference. Compared to cameras operating in the visual spectrum, they are advantageous due to their ability to see in total darkness, robustness to illumination variations, and less intrusion on privacy. Short-term single-object (STSO) tracking in thermal images is a challenging problem relevant in a growing number of applications. The work describes some application to recognizes the seed qualities, and presents evaluation results with the simulation on the STSO. This work describes various Edge Detection techniques, and gives the background required to understand the concept of Edge Detection methods. The literature surveys cover the kernel-based methods. Finally, Sobel Edge Detection has been presented, and used to identify the destination objects.
Influence of the Secondary Arc on the Operation of Single Phase Autoreclosure of the 400 kV interconnection between Hungary and Croatia

SUMMARY Faults on EHV lines are generally single-phase-to-ground ones and not permanent in the majority of cases. Thus single phase auto reclosure (SPAR), at which the faulty phases are tripped for a short time, eliminates the predominant part of the faults [1,2]. The secondary arc, which follows the high power arc after tripping the faulty phases at both side of the line may endanger the successfulness of reclosing if the duration of the switched off interval (dead time) is not long enough to ensure the extinction of the arc. The secondary arcing times recorded on different EHV lines or measured in laboratory tests show significant spread, consequently, to select a dead time according to the longest experimental secondary arc extinction time is not feasible. During commissioning of the double circuit 420 kV interconnection between Hungary and Croatia several staged faults were initiated to analyze the arc extinction performance. Initially the line was in operation by connecting the two circuits in parallel along the 1/3 rd of the full length. In this configuration the longest secondary arc extinction time was 4 seconds and the secondary arc has not extinguished in 27s in one of the tests, so the line had to be tripped out to clear the staged fault. Later on, the length of the Croatian section of the line has been significantly shortened after putting a new substation into service. The increased performance of SPAR of the new arrangement has been proved with field tests. re-ignitions in the arc channel produce transient wave processes along the line.
Supplementary Material for “Towards Discovering the Effectiveness of Moderately Confident Samples for Semi-Supervised Learning”

Performance on larger datasets. We also do experiments on the larger, challenging STL-10 [1] dataset, which has a distribution gap between labeled and unlabeled data. STL10 consists of 5, 000 labeled RGB images of size 96 × 96 and 100, 000 unlabeled images. The labeled set is split into ten pre-defined folds of 1, 000 images each. We evaluate on five of these ten folds. Under the same baseline condition, our proposed GSF improves over FixMatch [2] by a large margin (2.89%), verifying its efficacy on the difficult task. Due to time limit, it is difficult to present here additional experiments on more large datasets (e.g., ImageNet); we expect that performance improvement over FixMach would be similarly obtained. We will try to acquire additional results soon. Time cost of model training. Under the same baseline condition and on STL-10, the training efficiencies of GSF, PPF, and FixMatch are respectively 1.05 it/s, 1.16 it/s, and 1.20 it/s (‘it’ means a training iteration); their memory consumption is almost identical. Clarification on the design of GSF. GSF uses the gradient of features outputted by the feature extractor, since 1) they are at the highest abstraction level and thus contain rich information of global context and semantics, 2) they are in the feature space that we aim to learn and are thus closely related to optimization dynamics, and 3) their dimension is much lower than that of input space, enabling lower computation. Clarification on the design of PPF. We use Euclidean distance that gives improved performance over cosine similarity (e.g., by 3.87% on CIFAR-10 with 40 labels). Spectrum of confidences for samples selected by GSF and PPF. We show here the confidence spectrum in Fig. S1. Relationship between GSF and PPF. Features used by PPF are static data points in the learned feature space, which
PolyVI: Deep Generative Models for Gene Expression, Chromatin Accessibility, and Surface Protein Expression Data

Single-cell multimodal sequencing methods, which measure multiple different modalities si-multaneously (such as gene expression, chromatin accessibility, and surface protein data) are an exciting new space in the field of genomics as they provide a more comprehensive picture of cellular state than technologies that assay a single modality. Here I present PolyVI, a suite of three deep generative models to analyze DOGMA-seq (gene expression, protein, chromatin), ASAP-seq (chromatin, protein), and SNARE-seq (gene expression, chromatin) datasets. PolyVI is able to map the data to a low dimensional latent space, batch correct, and de-noise the data.
Leader–Follower Interpersonal Emotion Management: Managing Stress by Person-Focused and Emotion-Focused Emotion Management

Compliance with demands to express certain emotions at certain times is difficult for most employees to achieve without also experiencing adverse effects such as stress. Emotion researchers typically study “demands” in the form of organizational display rules. However, most emotional “demands” come from leaders who are uniquely positioned to help subordinates manage negative emotions and express positive ones. While this is often implied in the literature, research projects on leader-facilitated emotion management are scarce. Using Cóté’s social interaction model as a theoretical foundation, we examine leader-facilitated emotion management in a simulated workplace setting. Interpersonal, as opposed to intrapersonal, emotion management is a multifaceted process, whose success depends on emotion-focused action and socioaffective needs being met. Our hypotheses rested on this premise, and we sought to find the most effective combination of two common emotion-focused emotion management strategies (i.e., reappraisal and suppression) with a person-focused emotion management strategy (i.e., leader empathy) for helping participants to minimize stress from an affective event. We compared these relationships across a simulated crisis situation and under normal circumstances. Our results highlight the fundamental differences between interpersonal and intrapersonal emotion management. Suppression was found to be an effective strategy for lowering employee stress after an emotional event and expression of an active, negative emotion (i.e., anger) as long as the leader also displayed empathy. Under times of crisis, the empathy-suppression combination appeared to be especially effective. Implications are discussed.
En-Compactness: Self-Distillation Embedding & Contrastive Generation for Generalized Zero-Shot Learning

Generalized zero-shot learning (GZSL) requires a classifier trained on seen classes that can recognize objects from both seen and unseen classes. Due to the absence of unseen training samples, the classifier tends to bias towards seen classes. To mitigate this problem, feature generation based models are proposed to synthesize visual features for unseen classes. However, these features are generated in the visual feature space which lacks of discriminative ability. Therefore, some methods turn to find a better embedding space for the classifier training. They emphasize the inter-class relationships of seen classes, leading the embedding space overfitted to seen classes and unfriendly to unseen classes. Instead, in this paper, we propose an Intra-Class Compactness Enhancement method (ICCE) for GZSL. Our ICCE promotes intra-class compactness with inter-class separability on both seen and unseen classes in the embedding space and visual feature space. By promoting the intra-class relationships but the inter-class structures, we can distinguish different classes with better generalization. Specifically, we propose a Self-Distillation Embedding (SDE) module and a Semantic-Visual Contrastive Generation (SVCG) module. The former promotes intra-class compactness in the embedding space, while the latter accomplishes it in the visual feature space. The experiments demonstrate that our ICCE outperforms the state-of-the-art methods on four datasets and achieves competitive results on the remaining dataset.
Factors Affecting the Adoption of E-Procurement Practices in Public Sector in Kenya: (A Case of the Department Of Refugee Affairs in Dadaab)

The purpose of the study was to establish the factors affecting the adoption of e-procurement practices in public sector in Kenya, a case of the department of refugee affairs in Dadaab with an aim of making recommendations on proper adoption of e-procurement practices. The study aimed to establish how technological infrastructure, technical skills, ethical malpractice and noncompliance affect adoption of e-procurement. To achieve this, the researcher reviewed both theoretical and empirical literature and proposed the research methodology that addressed the gaps identified in literature as well as answer the stipulated research questions. This research study adopted a descriptive research design approach. The researcher preferred this method because it allows an in-depth study of the subject. A census was conducted where all the 102 staff in procurement department was issued with questionnaires. Data was collected using self-administered questionnaires. The data collected was analyzed by use of descriptive and inferential statistics. Multiple regression models was used to show the relationship between the dependent variable and the independent variables. The quantitative data generated was keyed in and analyzed by use of Statistical Package of Social Sciences (SPSS) version 22 to generate information which was presented using tables, charts, frequencies and percentages.
Error Correction Coding: Mathematical Methods and Algorithms

Preface. List of Program Files. List of Laboratory Exercises. List of Algorithms. List of Figures. List of Tables. List of Boxes. PART I: INTRODUCTION AND FOUNDATIONS. 1. A Context for Error Correcting Coding. PART II: BLOCK CODES. 2. Groups and Vector Spaces. 3. Linear Block Codes. 4. Cyclic Codes, Rings, and Polynomials. 5. Rudiments of Number Theory and Algebra. 6. BCH and Reed-Solomon Codes: Designer Cyclic Codes. 7. Alternate Decoding Algorithms for Reed-Solomon Codes. 8. Other Important Block Codes. 9. Bounds on Codes. 10. Bursty Channels, Interleavers, and Concatenation. 11. Soft-Decision Decoding Algorithms. PART III: CODES ON GRAPHS. 12. Convolution Codes. 13. Trefils Coded Modulation. PART IV: INTERATIVELY DECODED CODES. 14. Turbo Codes. 15. Low-Density Parity-Check Codes. 16. Decoding Algorithms on Graphs. PART V: SPACE-TIME CODING. 17. Fading Channels and Space-Time Coding. References. Index.
Assessment in Schizotypy: A Systematic Review Towards Clinical and Personality Models

Abstract. Introduction: research carried out regarding the psychological concept of schizotypy responds to a ﬁeld of extensive development since its conceptualization decades ago, which includes schizophrenia spectrum disorders (SSD) and schizotypal personality disorder (SPD). However, controversies still persist marked by the diﬃculty of establishing deﬁnitive consensus. Objective: the research purpose aimed to synthesize the empirical evidence involved in the use of various methodologies and tools for understanding schizotypy. Methodology: A systematic review was carried out in the following databases: Web of Science, Scopus, PubMed, Taylor and Francis, Wiley, Science Direct, Google Scholar, and PsycNet. By using the PRISMA guidelines, 65 studies published in the last decade (2010-2020) were discriminated. Results: The synthesis of cumulative knowledge made it possible to deﬁne schizotypy as an integrating multidimensional construct and to identify valid assessment tools in use, in its psychometric, clinical, and personality characterizations. Discussion/Conclusions: Clinical implications found in these reports are addressed, as well as notions of the continuum of psychoses, taxonomic, and multidimensional models.
Dowsing: Anchoring in Time

Purpose: To determine if 10 long term map dowsers could perceive water more accurately than 10 age and sex matched controls Methods/Materials: Ten map dowsers who had more than 20 years individual experience from a local dowsing club and ten age and sex matched controls from the local area general population were recruited. Participants were consented and baseline questionnaires were administered. Computer generated placement of water buckets was run for each of the subjects’ trials. The one bucket containing water was placed with nine other matching buckets under a tarp which had numbers on the top representing the placement of each bucket and was moved each trial. Each subject did two practice and ten real trials. Participants were asked to mark the bucket placement on a score sheet. Experimenter was positioned 15 feet from the buckets to reduce field interactions. Dowsers were able to use any type of dowsing equipment that they wished. Control subjects were provided with a choice of wooden or stone pendulum, wooden stick, or copper rods. Controls were given no training on the dowsing tools. Based on anecdotal reports of difficulty, selected buckets were made of a ¼ inch acrylic and plastic mix, considered the most difficult material through which to perceive a substance. Original analysis showed 100% failure in the dowsing group and 30% success in the control. As this was a statistical surprise, the data were reviewed. Based on information from Betz (1995), the next subjects’ placement was reviewed. It was determined that participants had accurately predicted the placement which was run for the next person and not for themselves. Further, they had completed their testing prior to the randomization being run. The final subjects’ randomization was run after this reanalysis process was suggested by the data and it too matched the results. The results suggest that in order for subject 1 to perceive subject 2 bucket placement, some change in the awareness of time or level of precognition may have been engaged. Results of p<0.0004 for the active group vs. p<0.3173 for the control group when analysis was done on the next subjects’ trial suggest that a larger pilot study should be done. A possible precognitive process may be involved. A three-arm trial that included standard dowsers, map dowsers, and control subjects would help to clarify if the movement forward in time or precognition is specific to map dowsers. In addition, sub-groupings could be done with less experienced dowsers to discover if this is a skill developed over time.
State of the art on organizational longevity: a systematic literature review

Low business longevity is a worldwide phenomenon afflicting multiple organizations that operate in disparate economic conditions, across various industrial processes, different in size and disparate cultural environments. The opening and internationalization of the economy, as well as the problems associated with the environmental crisis, exacerbate this situation. The ability to adapt and strategic renewal of organizations thus become a fundamental capability to survive in times of globalization and competitiveness. The paper’s objective is to present the state of the art of organizational longevity based on a Systematic Literature Review in two databases (WOS and Scopus) with the help of two software specialized in bibliometric analysis and scientific mapping (Bibliometrix and VOSviewer). Organizational longevity is a growing and consolidating field since it addresses a core problem within management, economics, and the social sciences in general: survival. A trend starts to emerge, which hints at connections between the longevity of organizations and the corporate social responsibility (CSR) practices that they engage in. work explores a path that combines bibliometric elements with elements from scientific mapping, and this allows analyzing the performance of authors, institutions, and countries, as well as reveals structures and dynamics in the field of management and organizations. They resort to five methods to carry out their work.
A Competent Convolutional Sparse Representation Model for Pan-Sharpening of Multi-Spectral Images

Two types of images are produced by Earth observation satellites, each having complementing spatial andspectral characteristics. Pan-sharpening (PS) is based on remote sensing and image fusion approach thatproduces a high spatial resolution multi-spectral image by merging spectral information from a low spatialresolution multispectral (MS) image with intrinsic spatial details from a high spatial resolution panchromatic(PAN) image. Traditional pan-sharpening methods continue to seek for a fused image that contains thenecessary spatial and spectral information. This work proposes a pan-sharpening method based on a recentinvention, convolutional sparse representation (CSR). Geometric structural characteristics are extracted fromthe PAN image using a CSR-based filtering procedure. The challenge of learning filters, convolutional basispursuit denoising (CBPDN), is handled using a modified dictionary learning method based on the concept ofAlternating Direction Method of Multipliers (ADMM). The retrieved details are put into MS bands usingapplicable weighting coefficients. Because the proposed fusion model avoids the standard patch-basedmethod, spatial and structural features are preserved while spectral quality is maintained. The spectraldistortion index SAM and the spatial measure ERGAS improve by 4.4 and 6.2 percent, respectively, whencompared to SR-based techniques. The computational complexity is reduced by 200 seconds when compared to the most recent SR-based fusion technique. The proposed method's efficacy is demonstrated by reduced-scale and full-scale experimental findings utilising the QuickBird and GeoEye-1 datasets.
Utilizing Deep Learning for Mapping Dozer Lines from Aerial Imagery

This article explores the utility of using aerial imagery and deep learning to automate dozer line mapping. Due to the lack of dozer line data, we leveraged dirt and gravel road data to estimate dozer line characteristics and trained deep learning networks. We examined the performance of deep neural networks composed of UNET decoders with different ResNet encoders. Preliminary results demonstrated that a deep neural network composed of ResNet-18 and UNET provided an overall IOU score of 0.91 when used to map dirt and gravel roads in Sonoma County during the 2017 North Tubbs Fire.
True New Agenda for South East Europe and EU

The purpose of this paper is to explain, in a clear and concise way, how the EU’s enlargement policy is affecting western Balkans countries. It aims to give an overview of how the EU strategy has grown since this idea is created. Where it stands today after having undergone its most important round of enlargement and what the perspective of accepting new members in the future is. The proposed paper examines how is proceeding EU with its next enlargement round in the conditions of so called “enlargement fatigue” that has gripped the European Union, with the global financial and economic crisis drawing much attention away from the Balkans accession issue. And in the other hand, as a result of this if inhabitants of the Western Balkans are becoming increasingly aware that enlargement is no longer fashionable in the EU. The paper discusses about the fact that EU member states should strongly reaffirm their Thessaloniki commitment, and so even further by giving their support to EU accession proposals for the western Balkans in the European Council. Furthermore it addresses the problem of the completion of the process in the region, the commitment of the EU in the front of the Balkans European future, the major challenges that have to affront both of them, the problems in the European home, and the duties of Western Balkans countries.Despite a number of unresolved issues in the region, one can see a new spirit of rapprochement and genuine cooperation in the Western Balkans enlargement is a strategic policy decision of the European Union. Elaborating the advancement measures to sustain the current rapprochement policy over the period ahead is a priority. The moment must be seized and the trend must be translated into an irreversible process. This is where South-East Europe and the European Union could find a true new agenda.
Few-shot Learning with Noisy Labels — Supplemental Material —

We include supplemental material for our work here. Sec. A shows the mislabeled samples in the 5-way 5-shot support set in Fig. 1 of the main paper, as well as two more example noisy support sets. We discuss computational complexity considerations for iteratively solving for the median in Sec. B. In Sec. C, we investigate noisy few-shot performance for different numbers of shots from the 5-shot setting considered in Sec. 6.2 of the main paper. Sec. D contains descriptions and additional implementation details of the baselines that we compare against. We perform further ablation studies beyond Sec. 6.4 of the main paper, investigating feature extractors, hyperparameter settings, and architectural design choices of TraNFS in Sec. E.
Community composition and species diversity of fruit-eating-insects of Gymnacranthera paniculata, Macaranga aleuritoides and Mastixiodendron pachyclado in a Papua New Guinea Primary Forest

— Community composition and species diversity of fruit-eating-insects were studied in a primary forest at Wanang, Madang, Papua New Guinea (PNG) using fruits regularly sampled and insects attacking them reared, preserved and identified. Sampling was done in different areas of the forest including low and high abundance of the host trees. Fruits of three predominant host trees, G. paniculata (Myristicaceae), M. aleuritoides (Euphorbiaceae) and M. pachyclados (Rubiaceae) were regularly collected and insects associated with them studied. The emergence from the fruits were 13 insect families and 16 species from G. paniculata, 17 insect families and 21 species from M. aleuritoides and 17 insect families and 25 species from M. pachyclados. Diversity assessment showed M. pachyclados was more diverse (H=2.0258) followed by G. paniculata (H=2.007). M. aleuritoides was the least diverse (H=1.443). A high percentage of scavengers and wood eaters were found in G. paniculata and M. aleuritoides. In M. pachyclados, more seed eaters, chewers and parasitoids were found instead. These results have implications for management of the community composition and diversity of the fruit-eating insects of the three host three species.