Miracle-dz/UniFAR-FTUs huggingface dataset:
Online false discovery rate control for anomaly detection in time series

This article proposes novel rules for false discovery rate control (FDRC) geared towards online anomaly detection in time series. Online FDRC rules allow to control the properties of a sequence of statistical tests. In the context of anomaly detection, the null hypothesis is that an observation is normal and the alternative is that it is anomalous. FDRC rules allow users to target a lower bound on precision in unsupervised settings. The methods proposed in this article overcome short-comings of previous FDRC rules in the context of anomaly detection, in particular ensuring that power remains high even when the alternative is exceedingly rare (typical in anomaly detection) and the test statistics are serially dependent (typical in time series). We show the soundness of these rules in both theory and experiments.
Time-Delayed Feedback Channel Design: Discrete Time H 1 Approach

—This paper proposes a method of improving performance of scalar discrete-time systems with substantial delay by adding additional delayed feedback channels (i.e. imposing a distributed delay feedback). The optimal weights for the added feedback channels are found using optimization techniques. In particular, we reduce the H 1 norm of the closed loop transfer function with multiple delayed feedback using techniques from static output feedback design. We impose constraints on the feedback gain in order to highlight the effectiveness of the distribution. In this manner, improvement on performance is a result of the distribution and not a change in the overall effective gain. The concept of applying a multiple delayed feedback channel is inspired by biological systems, where substantial delays can be present in feedback control. To show the effectiveness of this idea we apply our method to an example of a scalar genetic autoregulatory network. The constraint on the gain allows one to implement the feedback in a genetic regulatory network without having to change the reaction rates. A possible method of synthesizing such a system in a wet lab is explained in more detail. Finally, stability results indicate the possibility of stabilizing an unstable system with added delayed feedbacks (by adding larger delays). This approach may also be applicable to systems with large delays in which simple controllers are needed due to limitations in computational power. This paper motivates and provides preliminary results towards direct design of purely delay based controllers for network systems with large delays.
Research Efforts and Challenges in Crowd-based Requirements Engineering: A Review

Eliciting software system development requirements is a challenging task as the information is from various resources. The most constructive resource is the stakeholders of the system to be developed. It is critical yet timeconsuming to capture essential requirements to realize a reliable and workable software system. The crowd-based Requirements Engineering (crowd-based RE) approach adapts the crowdsourcing technique to access an extensive range of stakeholders and save time, especially for the generic type system with no clear stakeholder. This paper presents current research efforts and challenges in crowd-based RE. A systematic literature review method is adopted to explore literature based on two specific research questions. The first question aimed at identifying research efforts on crowd-based RE, and the second question focused on the main challenges discovered in pursuing crowd-based RE. The findings from the literature review show that many efforts have been made to explore and further improve crowd-based RE. This paper provides a foundation to pursue research in improving crowdsourcing techniques for the benefit of requirements engineering. Keywords—Crowd-based requirement engineering; requirements engineering; requirements elicitation; software engineering; crowdsourcing; review
MetaTS: Meta Teacher-Student Network for Multilingual Sequence Labeling with Minimal Supervision

Sequence labeling aims to predict a fine-grained sequence of labels for the text. However, such formulation hinders the effectiveness of supervised methods due to the lack of token-level annotated data. This is exacerbated when we meet a diverse range of languages. In this work, we explore multilingual sequence labeling with minimal supervision using a single unified model for multiple languages. Specifically, we propose a Meta Teacher-Student (MetaTS) Network, a novel meta learning method to alleviate data scarcity by leveraging large multilingual unlabeled data. Prior teacher-student frameworks of self-training rely on rigid teaching strategies, which may hardly produce high-quality pseudo-labels for consecutive and interdependent tokens. On the contrary, MetaTS allows the teacher to dynamically adapt its pseudo-annotation strategies by the student’s feedback on the generated pseudo-labeled data of each language and thus mitigate error propagation from noisy pseudo-labels. Extensive experiments on both public and real-world multilingual sequence labeling datasets empirically demonstrate the effectiveness of MetaTS.
Synchronization of the ovulation (OvSynch) in beef cattle

Todorova, T. , Todorova, Ts. & Todorov, I. (2021). Synchronization of the ovulation (OvSynch) in the beef cattle. Bulg. J. Agric. Sci., 27 (5), 996–1001 The OvSynch protocol itself has not been successfully used to synchronize beef cows in postpartum anestrus. Recently, various modifications to the ovulation synchronization methods have been developed. One of these is the so-called PreSynch. Cows that are unresponsive to the first injection of GnRH may come to estrus early (36 to 48 hours before prostaglandin injection) or have follicles that are not “synchronized” at the time of prostaglandin injection. Pre-synchronization, as the name indicates, is a protocol that “pre-synchronizes” cows to the early stages of the cycle for optimal response to GnRH treatment, and thus improves ovulation synchronization results (OvSynch). The experimental cows were divided into three groups of 12 animals each. One group was treated per week. The cows were artificially inseminated. After 35 days, if the cow did not come to spontaneous oestrus, a sonographic examination was performed to determine pregnancy. A total of 36 animals were treated. Out of 36 cows, 17 (47.2%) were pregnant after one treatment and the pregnancies are ongoing. Nineteen cows, unfertilized after the first synchronization, were exposed 45 days later for another treatment called modified OvSynch. The modified OvSynch includes presynchronization before OvSynch (1 or 2 injections of PGF2α given 14 days apart, with the first injection of OvSynch given 12 to 14 days after the second injection of PGF2α or injections of PGF2α and GnRH every 7 days. Out of nineteen, 11 (57.9%) cows were pregnant 35 days after insemination.
Efficient Authentication Algorithm for Secure Remote Access in Wireless Sensor Networks

Wireless sensor networks convey mission critical data that calls for adequate privacy and security protection. To accomplish this objective, numerous intrusion detection schemes based on machine learning approaches have been developed. In addition, authentication and key agreements techniques have been developed using techniques such as elliptic curve cryptography, bilinear pairing operations, biometrics, fuzzy verifier and Rabin cryptosystems. However, these schemes have either high false positive rates, high communication, computation, storage or energy requirements, all of which are not ideal for battery powered sensor nodes. Moreover, majority of these algorithms still have some security and privacy challenges that render them susceptible to various threats. In this paper, a WSN authentication algorithm is presented that is shown to be robust against legacy WSN privacy and security attacks such as sidechannel, traceability, offline guessing, replay and impersonations. From a performance perspective, the proposed algorithm requires the least computation overheads and average computation costs among its peers.
Food and nutrition insecurity and risk factors for chronic noncommunicable diseases among solid waste collectors

Previous studies have shown a relationship between food and nutrition insecurity and chronic noncommunicable diseases (NCDs), and it should be noted that nutritional deficiencies and hunger are not the only forms of expression of this condition. The aim of this study was to evaluate the occurrence of food and nutrition insecurity and the risk factors for the development of NCDs among solid waste collectors in a municipality in the countryside of Ceará State (CE), Brazil. A cross-sectional study was conducted with 32 workers, aged 20-66 years. A structured questionnaire was applied to collect personal, socioeconomic, dietary and anthropometric data. Food and nutrition insecurity was evaluated through the Brazilian Food Insecurity Scale (EBIA). There was food and nutrition insecurity in 100% of the sample, low level of education (78.12%) and low income (87.5%), prevalence of overweight (25.0%) and obesity (31.26%) and poor consumption of fruits, vegetables and vegetables. The was a high proportion of moderate and severe food insecurity, and workers presented risk factors for the onset of NCDs.
Shapley-value based inductive conformal prediction

Shapley values of individual instances were recently proposed for the problem of data valuation. They were deﬁned as the average marginal instance contributions to the performance of a given predictor. In this paper we propose to use Shapley values of individual instances as conformity scores. To compute these values eﬃciently and exactly we employ a standard algorithm based on nearest neighbor classiﬁcation and propose a variant of this algorithm for clustered data. Both variants are used for computing Shapley conformity scores for inductive conformal predictors. The experiments show that the Shapley-value conformity scores result in smaller prediction sets for signiﬁcance level (cid:15) ≤ 0 . 1 compared with those produced by standard conformity scores (i.e. similarity between true and predicted output values).
A Dual-Bridge Hybrid DC Circuit Breaker

Various DC circuit breakers (DCCBs) have been widely proposed for the DC fault protection of high-voltage direct-current (HVDC) grids. In recent years, hybrid DCCBs (HCBs) have been paid significant attentions due to their features of low power losses and fast dynamic response. However, several aspects regarding the design of HCB should be further addressed. For instance, the requirement of deploying a large surge arrester to dissipate the large fault current energy should be further addressed and the strategy to perform zero-voltage switching (ZVS) of semiconductor devices during the post-fault restoration processes should be investigated. In this paper, a dual-bridge hybrid DC circuit breaker (DB-HCB) with freewheeling diode branches is proposed to address the above issues. The operation principle of the proposed DB-HCB for pole-to-ground and pole-to-pole faults is presented. Compared with other HCBs, the capacity of the surge arrester is obviously reduced, so that the capital cost and volume of the proposed DB-HCB is decreased. Moreover, the ZVS is implemented during the post-fault restoration processes. Simulation results in PSCAD/EMTDC are given to validate the effectiveness of the proposed DB-HCB.
JPPE 24권5호_4차.hwp

Background and objective: The prolonged COVID-19 pandemic has had significant impacts on mental health, which has emerged as a major public health issue around the world. This study aimed to analyze trends and network structure of ‘grow-your-own (GYO)’ through Instagram, one of the most influential social media platforms, to encourage and sustain home gardening activities for promotion of emotional support and physical health. Methods: A total of 6,388 posts including keyword hashtags ‘#gyo’ and ‘#growyourown’ on Instagram from June 13, 2020 to April 13, 2021 were collected. Word embedding was performed using Word2Vec library, and 7 clusters were identified with K-means clustering: GYO, garden and gardening, allotment, kitchen garden, sustainability, urban gardening, etc. Moreover, we conducted social network analysis to determine the centrality of related words and visualized the results using Gephi 0.9. 2. Results: The analysis showed that various combinations of words, such as #growourrownfood, #growourrownveggies, and #growwhatyoueat revealed preference and interest of users in GYO, and appeared to encourage their activities on Instagram. In particular, #gardeningtips, #greenfingers, #goodlife, #gardeninglife, #gardensofinstagram were found to express positive emotions and pride as a gardener by sharing their daily gardening lives. Users were participating in urban gardening through #allotment, #raisedbeds, #kitchengarden and we could identify trends toward self-sufficiency and sustainable living. Conclusion: Based on these findings, it is expected that the trend data of GYO, which is a form of urban gardening, can be used as the basic data to establish urban gardening plans considering each characteristic, such as the emotions and identity of participants as well as their dispositions.
Towards the factory 4.0 Convergence and divergence of lean models in Italian automotive plants

This paper studies the interplay in terms of techno-organisational change between the adoption of I4.0 technologies and lean production systems. Leveraging on the results of two field-work analyses conducted under a collaboration with the Sabattini Foundation and the metal workers trade union FIOM in the period 20162018, we compare an ensemble of factories producing both high-end/highly customised and low-end products. Emerging patterns of convergence and divergence in the techno-organisational configurations of these factories confirm that this wave of technological innovation is far from leading to total automation or the digital revolution. On the contrary, it appears to be integrated into the historical trend of “leanification” of production processes in the automotive sector, despite the organisational variety shaped by the actual implementation of this production model.
IMAGE BASED BIRD SPECIES IDENTIFICATION USING DEEP LEARNING

: Nowadays, it has become a hobby of watching birds for human beings. Due to the different features of the bird with their color, size, and viewing angle of humans are unable to identify and classify the bird species. In this paper, we present a new image classification method for bird species identification. we are extracting the features of a bird and with novel preprocessing and data augmentation methods, we train a convolutional neural network on the biggest publicly available dataset. Our network architecture achieves a mean average precision score of 0.95 when predicting the main species of each image file and scores 0.96, 0.99 chances of bird species from the dataset.
On Privacy and Confidentiality of Communications in Organizational Graphs

Machine learned models trained on organizational communication data, such as emails in an enterprise, carry unique risks of breaching confidentiality, even if the model is intended only for internal use. This work shows how confidentiality is distinct from privacy in an enterprise context, and aims to formulate an approach to preserving confidentiality while leveraging principles from differential privacy. The goal is to perform machine learning tasks, such as learning a language model or performing topic analysis, using interpersonal communications in the organization, while not learning about confidential information shared in the organization. Works that apply differential privacy techniques to natural language processing tasks usually assume independently distributed data, and overlook potential correlation among the records. Ignoring this correlation results in a fictional promise of privacy. Naively extending differential privacy techniques to focus on group privacy instead of record-level privacy is a straightforward approach to mitigate this issue. This approach, although providing a more realistic privacy-guarantee, is over-cautious and severely impacts model utility. We show this gap between these two extreme measures of privacy over two language tasks, and introduce a middle-ground solution. We propose a model that captures the correlation in the social network graph, and incorporates this correlation in the privacy calculations through Pufferfish privacy principles.
Rare and Endangered Plant Leaf Identification Method Based on Transfer Learning and Knowledge Distillation

Considering the limited sample size of rare and endangered plant leaves and the issue that leaf identification is mainly conducted using mobile smart devices and other technology with low computing power, this paper proposes a rare and endangered plant leaf identification method based on transfer learning and knowledge distillation. Following the expansion of data sets containing rare and endangered plant leaves, the last fully connected layer was replaced with trained Alexnet, VGG16, GoogLeNet, and ResNet models to conduct transfer learning, and realized a relatively high success rate in identifying images of these species. Then, knowledge distillation was utilized to transfer Alexnet, VGG16, GoogLeNet, and ResNet models into a lightweight model. The experiment results indicate that, compared with other methods, the lightweight rare and endangered plant identification model trained with the methods described in this paper was not only more accurate but also less complex than its alternatives.
Disaster Risk Governance: Effects of Floods on Small Businesses in Jhang and Dera Ghazi Khan Districts, Pakistan

Adapting to future climatic variability in semi-arid and arid areas requires devising effective adaptive strategies across sectors. It is in this context that this research surveyed two districts, Dera Ghazi Khan and Jhang in Pakistan, and investigated the impact of floods on small businesses through vulnerability analysis. The findings showed that flood damage to infrastructure and capital goods was debilitating for small businesses. Moreover, loss of utilities like electricity and transport was a significant hindrance in resuming business operations. Stocks and access to credit were deemed to be essential for ensuring quick recovery. In addition, the role of extended family, neighbours, police and Non-Governmental Organisations, was also found to be integral. Furthermore, risk was exacerbated due to the lack of effective early warning systems in communities. Therefore, decision-making was often based on existing power structures and tended to accentuate risks in flood-affected areas.
Brain Tumor Classification of MRI Images Using Deep Convolutional Neural Network

Manual tumor diagnosis from magnetic resonance images (MRIs) is a time-consuming procedure that may lead to human errors and may lead to false detection and classification of the tumor type. Therefore, to automatize the complex medical processes, a deep learning framework is proposed for brain tumor classification to ease the task of doctors for medical diagnosis. Publicly available datasets such as Kaggle and Brats are used for the analysis of brain images. The proposed model is implemented on three pre-trained Deep Convolution Neural Network architectures (DCNN) such as AlexNet, VGG16, and ResNet50. These architectures are the transfer learning methods used to extract the features from the pre-trained DCNN architecture, and the extracted features are classified by using the Support Vector Machine (SVM) classifier. Data augmentation methods are applied on Magnetic Resonance images (MRI) to avoid the network from overfitting. The proposed methodology achieves an overall accuracy of 98.28% and 97.87% without data augmentation and 99.0% and 98.86% with data augmentation for Kaggle and Brat's datasets, respectively. The Area Under Curve (AUC) for Receiver Operator Characteristic (ROC) is 0.9978 and 0.9850 for the same datasets. The result shows that ResNet50 performs best in the classification of brain tumors when compared with the other two networks.
Risky choice framing by experience: A methodological note

In classic research on judgment and decision making under risk, risk is described by providing participants with the respective outcomes and probabilities in a summary format. Recent research has introduced a different paradigm – decisions-by-experience – where participants learn about risk by sampling from the outcomes, rather than by summary descriptions. This latter research reports a description-experience gap, indicating that some of the classic patterns of risk attitude reverse when people experience the risk. Recent research has attempted to investigate risky choice framing in the decisions-by-experience paradigm. I discuss how this research runs into problems in properly manipulating framing in decisions by experience. Drawing from framing research with animals, I argue that framing effects also exist in experience tasks. The classic Asian Disease task, however, awaits proper translation into an experience paradigm.
A proof of the Erdös-Faber-Lovász conjecture: Algorithmic aspects

The Erdos-Faber-Lovász conjecture (posed in 1972) states that the chromatic index of any linear hypergraph on $n$ vertices is at most n. Erdös considered this to be one of his three most favorite combinatorial problems and offered a $500 reward for a proof of this conjecture. We prove this conjecture for every large n. Here, we also provide a randomised algorithm to find such a colouring in polynomial time with high probability.
Self-Folding Non-Invasive Miniature Robots: Progress and Trend in the Biomedical Field

Developments in surgery have been geared toward minimizing the invasiveness of the procedure to improve both the treatment itself and the patient’s postoperative wellbeing. As such, attention has been directed toward reducing human error and miniaturizing clinical devices by developing smaller devices and robotic systems. While there have already been significant advancements in this area, apparatus can further benefit from being foldable, expandable, and further condensable. By promoting these characteristics, origami engineering, which extrapolates the fundamental principles of paper folding to real-world projects, has become increasingly prevalent in the biomedical field. This paper reviews the field of origami engineering, its fundamental mechanical and mathematical properties, and the recent progress in specific research areas. Then, this paper discusses several devices that have emerged over the past decade in detail based on their characteristics and implementations. Finally, this paper addresses the technical challenges and general research trend of selffolding non-invasive miniature robots.
The influence of paddle orientation on boat velocity in canoeing

The orientation of the paddle during the outrigger canoe stroke is an important factor in maximising propulsive forces. In order to investigate the influence of paddle orientation on boat velocity, a mathematical model of the outrigger canoe paddling stroke was established. The model accounted for the movement of the paddler within the boat, the motions of the paddle blade through the water, generating fluid forces, and the resistive forces acting on the boat and paddler. The model was validated against on water data for an elite female paddler, with a difference of only 1.3% between model output and measured on water data being found. Simulations were carried out to determine the influence of the paddle blade offset angle relative to the shaft. Maximum velocity was achieved in the condition where the paddle's angle of attack was closest to zero when peak paddle velocity occurred. This was found to be at an offset angle of -20 degrees between the shaft and blade of the paddle. For the paddler investigated here, the model outputs suggested that using a normal paddle with no offset angle between the paddle blade and shaft may not be optimal.