Miracle-dz/UniFAR-FTUs huggingface dataset:
The Myth of AI Failure

A myth has developed that AI has failed as a research program. Most myths contain some germ of truth but this one is exceptional in that it is more or less completely false. In fact AI is a remarkably successful research program which has delivered not only scientific insight but a great deal of useful technology. One of the main reasons why people assert the failure of AI as a research program is the mistaken view that its main goal is to replicate human intelligence. Such a view is understandable. It is common to misread Turing’s 1950 paper Computing Machinery and Intelligence as suggesting that the ultimate goal of AI should be the complete replication of human intelligence. AI researchers have also not done sufficient to make it clear that complete replication of human intelligence is not the ultimate goal of AI. A further source of the failure myth is the need felt by many researchers to distance their approach to AI from other, usually previous, approaches. In many cases a fashionable approach to AI may give itself a new name - ALife would be a good example – and portray previous AI approaches as having failed. In truth there is no failure to be explained. Almost every citizen in the developed world makes use of AI-derived technology every day. The fact that this AI technology is usually hidden in other technologies and works unobtrusively is a measure of just how successful AI has been. AI has also inspired, and continues to inspire, many other disciplines from linguistics to biology through the generation of scientifically useful data and concepts. The scientific work may still be at an early stage but its potential is great and failure myths should not be allowed to impede it.
STUDY ON KERNEL-BASED POSSIBILISTIC CLUSTERING AND DISSOLVED GAS ANALYSIS FOR FAULT DIAGNOSIS OF POWER TRANSFORMER

Dissolved gas analysis (DGA) is an important method to diagnose the fault of power transformer.Aimed at the problems existed in fuzzy c-means clustering algorithm which is applied in DGA,a simplified kernel-based possibilistic c-means clustering algorithm is presented.This algorithm combines kernel function with possibilistic c-means clustering,then is applied to DGA data analysis in power transformer.It's proved that this algorithm can cluster the samples fast and efficiently,and adapts to the environment of containing the noise samples.
Sentence embeddings for Quora question similarity

Duplicate questions are a common issue in community question answering forums. In this project, we work on the Quora Question Pairs dataset and aim to build a question similarity classifier to detect duplicate questions, in order to provide better Quora user experience. We explored different deep learning model architectures (LSTM [1], BERT [3]) with Siamese networks. Sentence-BERT[6] achieved 99.9% training accuracy and 89.7% dev/test accuracy. To alleviate the overfitting issue, we proposed to replace the softmax layer with a few fully-connected layers in order to capture more general correlations between sentence embeddings and similarity score. We also added a penalty to the similarity score for question pairs with high similarity scores but different question words. Finally, we achieved 91.56% test accuracy.
PROBLEMS OF TOURISM AND RECREATION

This paper is aimed to differentiate types of visitors’ behavior at one of the biggest event in Finland International Kotka Maritime Festival. Based on 367 questionnaires the research revealed that behavioral practices of local residents, domestic and international tourists differ, though not radically. Despite its international character the festival is more significant and interesting for locals, who spend more money and participate in more activities of the festival comparing to other groups. These trends can be explained by involvement of local residents and their families or friends into organisation of some of the festival’s events and possibility to spend time together. While developing a concept of the festival, organisers should consider a context and features of international tourists arriving to Kotka, but focus on raising level of satisfaction of the local community through more active engagement in preparation and conduction of the festival, diversification of the events and taking into account interests of all the stakeholders.
Analysis of cascading failures under interactions between power grid and communication network

The conventional research on cascading failures in the power system is only limited in the physical power grid,in which interactions between power grid and communication network are seldom considered.With IEEE 118-bus system and New England 39-bus system as examples,the model of interaction between power grid and its communication network in accord with the practical power system is presented based on the DC power flow model.The impact of various routing strategies on the cascading failures of power system is investigated by adjusting the responding parameters of communication network and the inter-similarity between coupled networks is discussed based on the complex network theory.Simulative results indicate that,with proper routing strategy of communication network,the probability of cascading failures of power system can be reduced and the optimal inter-similarity strategy between coupled networks of different power systems is same,which can be adopted to effectively decrease the probability of large-scale blackout.
Human Emotion Detection And Classification Using Convolution Neural Network

– Facial expression is the non-verbal communication that provides some related-information about the emotion of a person. Detecting and recognizing human emotion is a big challenge in computer vision and artiﬁcial intelligence. The main objective of this paper is to develop a robust technique that can detect and identify human emotions such as anger, sorrow, happiness, surprise, fear, disgust and neutral in real-time. In this paper, Viola–John’s algorithm has been proposed to detect the emotions more accurately. This algorithm is used for tracking the emotions in real time. In this paper, the real-time images are captured, and then features are extracted from the face images. Image enhancement and gradient detection are used after extracting the features of face images, then multiple feature extraction like GLCM, LBP and PCA are applied. All extracted features of face images are combined and compared with different databases to get accurate emotion state. CNN classifier is used to achieve conditional detection of accurate facial expressions.
Corporate Sector Leverage From Financial Stability Perspective1

Non-financial companies’ indebtedness has risen substantially following global financial crisis era due to increasing global risk appetite and capital inflows towards developing economies. However, reversal of capital flows owing to lower growth prospects of emerging countries, concerns about expansionary monetary policies of developed economies and surged in global risk aversion resulted in higher funding cost for emerging markets’ corporate sectors. In order to analyze potential systemic risk stemming from corporate sector’s foreign currency risk and roll-over capability, indebtedness should be monitored closely from sustainable financial stability perspective. This study aimed to estimate optimal debt level for non-financial companies listed on Borsa Istanbul Stock Exchange and estimated leverage ratio stood at reasonable levels in spite of the significant increase in indebtedness since global crisis. Even if indebtedness of corporate sector is assumed to be at reasonable levels, non-financial companies’ prudential approach for debt sustainability is considered to be important in terms of systemic risk mitigation. Moreover, since this study was conducted for aggregated terms for company sample, sector and company specific factors should also be considered to estimate optimal leverage ratio specifically.
Morphology, Histology and Serotonin Immunoreactivity on Salivary Glands of Stick Insect, Phobaeticus serratipes (Phasmida: Phasmatidae)

The salivary gland plays a significant role in physiological processes in insects including food lubrication, extra-oral digestion and enzyme secretion. This study was conducted to describe the morphology and histology of salivary glands of stick insect, Phobaeticus serratipes (Phasmida: Phasmatidae). The observation on gross morphology of salivary glands was photographed by using DSLR Canon EOS 6D camera attached to a stereo microscope. The histological study of the salivary glands involves special staining procedures of periodic Schiff’s acid reagent and Alcian blue method. The immunohistochemical study of the biogenic amine serotonin distribution was observed under fluorescence microscope Zeiss AxiocamMRm Apotome.2. Results showed that the salivary glands were the acinar type that consists of two cells, parietal cell and zymogenic cell. The serotonin immunoreactivity of the salivary glands which located on the nerve fibers and might act as a neurotransmitter.
Road deformation monitoring and event detection using asphalt‐embedded distributed acoustic sensing (DAS)

Distributed acoustic sensing (DAS) is a new technology that is being adopted widely in the geophysics and earth science communities to measure seismic signals propagating over tens of kilometers using an optical fiber. DAS uses the technique of phase‐coherent optical time domain reflectometry (φ‐OTDR) to measure dynamic strain in an optical fiber as small as nε by examining interferences in Rayleigh‐backscattered light. This technology is opening a new field of research of examining very small strains in infrastructure that are much smaller than what is currently able to be measured with the commonly used Brillouin‐based fiber optic sensing technologies. These small strains can be indicative of infrastructure's performance and use level. In this study, a fiber optic strain sensing cable was embedded into an asphalt concrete test road and spatially distributed dynamic road strain was measured during different types of loading. The study's results demonstrate that φ‐OTDR can be used to quantitatively measure strain in roads associated with events as small as a dog walking on the surface. Optical frequency domain reflectometry (OFDR), a widely implemented but less accurate distributed fiber optic strain monitoring technology, was also used along with traditional pavement strain gauges and 3D finite element modeling to validate the φ‐OTDR pavement strain measurements. After validation, φ‐OTDR strain measurements from various events are presented including a vehicle, pedestrian, runner, cyclist, and finally a dog moving along the road. This study serves to demonstrate the deployment of φ‐OTDR to monitor roadway systems.
Solving Disjunctive Temporal Networks with Uncertainty under Restricted Time-Based Controllability using Tree Search and Graph Neural Networks

Scheduling under uncertainty is an area of interest in artificial intelligence. We study the problem of Dynamic Controllability (DC) of Disjunctive Temporal Networks with Uncertainty (DTNU), which seeks a reactive scheduling strategy to satisfy temporal constraints in response to uncontrollable action durations. We introduce new semantics for reactive scheduling: Time-based Dynamic Controllability (TDC) and a restricted subset of TDC, R-TDC. We present a tree search approach to determine whether or not a DTNU is R-TDC. Moreover, we leverage the learning capability of a Graph Neural Network (GNN) as a heuristic for tree search guidance. Finally, we conduct experiments on a known benchmark on which we show R-TDC to retain significant completeness with regard to DC, while being faster to prove. This results in the tree search processing fifty percent more DTNU problems in R-TDC than the state-of-the-art DC solver does in DC with the same time budget. We also observe that GNN tree search guidance leads to substantial performance gains on benchmarks of more complex DTNUs, with up to eleven times more problems solved than the baseline tree search.
A multi-instance learning based approach for whitefly pest detection

Agriculture constantly faces various challenges including attacks from new pests and insects. With large farm sizes and plummeting manpower in the agricultural sector, it becomes challenging to continuously monitor crops for pest infestation. In this research paper, a specific type of pest attack known as the white fly attack has been investigated which affects a variety of crops. This paper presents four different approaches for automated classification of whiteflies which are the Bayesian network, convolution neural network (CNN), ResNet and multi-instance learning-CNN. A comparative analysis with conventional machine learning and deep learning techniques has also been presented. The performance of the proposed technique has been evaluated in terms of the classification accuracy. The experimental results obtained show that the proposed technique attains a classification accuracy of 95.53%, 96.9%, 97.6% and 98.13% for the four models respectively. A comparative analysis in terms of accuracy of classificaiton, with existing techniques shows that the proposed technique outperforms baseline deep learning models identifying whitefly infestation.
Effect Of Electrical Muscle Stimulation In Some Of The Bio-Kinetic Variables On Long Jum

Sport has been one of the main axes of human life since ancient times, and in recent years there has been a remarkable development of the development of answers and solutions to many of the questions that appeared at the time, This was evidenced largely by the positive gradation in the level of records, which has not stopped to the present time and in the effectiveness The long jump that depends on speed and strength is the efficiency of the long jump. The jumping event requires speed in the rough run and explosive force when upgrading and advancing in order to achieve the longest distance possible by jumping, So the importance of research is to know the effect of the use of electric stimulation The problem of research is the lack of real data on the amounts of the variables of the Quintet, which leads the basis in the development of Appropriate training programs to reach the peak of achievement and also through the researcher's knowledge of previous studies in the long jump, found the reliance on classical training methods without using other methods that can achieve better results The researcher conducted the field experiment and then the post-test and obtain real data to serve the sports field, the researchers entered the data to the census and get results to serve achievement, and the researchers concluded. 1The regulated diversity of the frequency values of electric stimulation in the development of strength for the muscles when seeking and pushing the long jump players and electric stimulation with the exercises of the trainer developed the achievement. The researchers recommended 1the importance of the adoption of sessions and training electrical stimulation at different levels of different in terms of intensity of current and the importance of the inclusion of the trainer's exercises with the sessions of stimulation of muscle groups.
ditto: WAN Traffic Obfuscation at Line Rate

—Many large organizations operate dedicated wide area networks (WANs) distinct from the Internet to connect their data centers and remote sites through high-throughput links. While encryption generally protects these WANs well against content eavesdropping, they remain vulnerable to trafﬁc analysis attacks that infer visited websites, watched videos or contents of VoIP calls from analysis of the trafﬁc volume, packet sizes or timing information. Existing techniques to obfuscate Internet trafﬁc are not well suited for WANs as they are either highly inefﬁcient or require modiﬁcations to the communication protocols used by end hosts. This paper presents ditto , a trafﬁc obfuscation system adapted to the requirements of WANs: achieving high-throughput trafﬁc obfuscation at line rate without modiﬁcations of end hosts. ditto adds padding to packets and introduces chaff packets to make the resulting obfuscated trafﬁc independent of production trafﬁc with respect to packet sizes, timing and trafﬁc volume. We evaluate a full implementation of ditto running on programmable switches in the network data plane. Our results show that ditto runs at 100 Gbps line rate and performs with negligible performance overhead up to a realistic trafﬁc load of 70 Gbps per WAN link.
Sleep Extension Improves Glycemic Control in Type 2 Diabetes with Obstructive Sleep Apnea

Background: The purpose of this study was to investigate whether sleep extension lowers blood glucose in patients with type 2 diabetes and chronic sleep restriction, and the moderating role of sleep apnea on the effect of sleep extension. Methods: This study enrolled 19 adults with type 2 diabetes and a sleep duration of less than 7 hours per night, of whom 16 (84.2 %) had obstructive sleep apnea. They were instructed to sleep at their usual time for 3 weeks as baseline, and then to increase their time in bed by 1 more hour per night for 3 weeks. Their sleep duration was recorded using actigraphy monitoring and sleep logs. Their fasting plasma glucose, glycated albumin, and glycated hemoglobin were measured before and after sleep extension. Results: Nine of the 19 (47.4 %) participants had a decrease in glycated albumin after sleep extension, and these participants had a significantly higher apnea-hypopnea index (AHI; p < 0.001). The participants with an AHI ≥ 30 times per hour were older (p = 0.04), had a shorter baseline total sleep time measured by actigraphy (p = 0.04), and had a more significant decrease in glycated albumin after sleep extension (p = 0.01). The AHI level was significantly correlated with the extent of decrease in glycated albumin after sleep extension (r = 0.72, p = 0.001). Conclusion: The current study showed the potential of 3-week sleep extension in decreasing blood glucose in patients with diabetes comorbid with severe obstructive sleep apnea and sleep deprivation.
CHARACTERISATION AND MEASUREMENT OF SILICON DETECTORS

An overview of the principal techniques and methods used to characterise silicon radiation detectors is given. These techniques allow to measure the most important electrical parameters of the detectors (leakage current, junction and inter–electrode capacitance, depletion voltage, etc.). The influence of these quantities on the detector's performance and on the achievable energy and position resolutions is discussed. Their knowledge is also of fundamental importance for the construction and understanding of the detector functional models. Experimental measurements illustrating the characterisation in the laboratory of some of the most used types of silicon detectors are given.
Young Carers’ Self-Compassion and Subjective Well-Being Relative to Non-Caregiving Youth

Young carers (YCs) provide unpaid care for their family members due to several circumstances within the family. Caregiving can be time consuming and quite difficult for some adolescents. By meeting others’ needs, their needs may be neglected, thereby potentially affecting their levels of self-compassion (SC) and subjective well-being (SWB). Due to a lack of studies in these areas, this descriptive, exploratory, quantitative, and comparative study aimed to fill in this gap. This study sampled 55 YCs and 107 non-YCs between the ages of 12-18 years. Multivariate analyses (MANOVA) were carried out. Results revealed that YCs and non-YCs had similar levels of SC and SWB. Implications for what it means for YCs and further support were discussed.
Real-time trajectory tracking control of mobile robot based on Backstepping time-varying state feedback and PID control method

According to the kinematic model of mobile robot using the thought of hierarchical con- trol, the tracking control of the mobile robot is divided into two parts: trajectory controller and ro- bot velocity PID controller. Based on Backstepping time-varying statefeedback method and Lyapunov theory, a global tracking control algorithm is presented by introducing the virtual feedback variable, which has the property of tangent. The PID velocity controller is used to meet the demand of real-time control of the speed of drive motors. Considering the dynamic contract of the mobile robot, the limited strategy is used to ensure the smooth motion of the robot. At last, the tracking control experiments are carried on the two-wheel driven mobile robot based on DSP. The experiment results are satisfied.
Evaluating the performance of a neuroevolution algorithm against a reinforcement learning algorithm on a self-driving car

Exploring various machine learning methods revealed that the traditional gradient-based learning algorithms such as the ones used in reinforcement learning might not be as eﬃcient with certain tasks as they might seem. By drawing inspiration from multiple state of the art examples, the idea of this project is comparing non-gradient-based algorithm such as neuroevo-lution with gradient-based reinforcement learning on an irregular task of training a car to self-drive around circuits with varying complexity. The raw quantitative data collected during evaluation show that neuroevolution is capable of producing solutions to this problem with great speed when compared to the reinforcement learning approach. However, when the reinforcement learning approach is allowed to train for long enough, it manages to train models that are outperforming even the ones created by neu-roevolution. Further statistical research is required to see whether the diﬀerences in the performances are signiﬁcant.
Generalized Omega Turn Gait Enables Agile Limbless Robot Turning in Complex Environments

Reorientation (turning in plane) plays a critical role for all robots in any field application, especially those that in confined spaces. While important, reorientation remains a relatively unstudied problem for robots, including limbless mechanisms, often called snake robots. Instead of looking at snakes, we take inspiration from observations of the turning behavior of tiny nematode worms C. elegans. Our previous work presented an in-place and in-plane turning gait for limbless robots, called an omega turn, and prescribed it using a novel two-wave template [1]. In this work, we advance omega turn-inspired controllers in three aspects: 1) we use geometric methods to vary joint angle amplitudes and forward wave spatial frequency in our turning equation to establish a wide and precise amplitude modulation and frequency modulation on omega turn; 2) we use this new relationship to enable robots with fewer internal degrees of freedom (i.e., fewer joints in the body) to achieve desirable performance, and 3) we apply compliant control methods to this relationship to handle unmodelled effects in the environment. We experimentally validate our approach on a limbless robot that the omega turn can produce effective and robust turning motion in various types of environments, such as granular media and rock pile.