Miracle-dz/UniFAR-FTUs huggingface dataset:
Quantifying the Degradation of Radio Maps in Wi-Fi Fingerprinting

One of the most common assumptions regarding indoor positioning systems based on Wi-Fi fingerprinting is that the Radio Map (RM) becomes outdated and has to be updated to maintain the positioning performance. It is known that propagation effects, the addition/removal of Access Points (APs), changes in the indoor layout, among others, cause RMs to become outdated. However, there is a lack of studies that show how the RM degrades over time. In this paper, we describe an empirical study, based on real-world experiments, to evaluate how and why RMs degrade over time. We conducted site surveys and deployed monitoring devices to analyse the radio environment of one building over 2+ years, which allowed us to identify significant changes/events that caused the degradation of RMs. To quantify the RM degradation, we use the positioning error and propose the RM degradation ratio, a metric to directly compare two RMs and measure how different they are. Obtained results show that the positioning performance is much better when RMs are collected on the same day as the test data, and although RM degradation tends to increase over time, it only leads to large positioning errors when significant changes occur in the Wi-Fi infrastructure, making previous RMs outdated.
Improved CNN-based Magnetic Indoor Positioning System using Attention Mechanism

Geomagnetic field fingerprinting is emerging as the most feasible and cost-effective alternative to WiFi and Bluetooth fingerprinting due to the omnipresence of magnetic field. With the increasing interest in applying deep learning methods to magnetic fingerprinting, attention mechanisms have been lately adopted to better recognize magnetic sequence patterns. In the last couple of years, several comparative studies have proved the added value of leveraging attention mechanisms in conjunction with Recurrent Neural Networks (RNNs) for indoor positioning systems treating sequential magnetic data. Yet, no study has been conducted to determine the performance contribution of these mechanisms to solutions based on Convolutional Neural Networks (CNNs). In this study, we propose a CNN-based indoor magnetic-only fingerprinting system using Recurrence Plots (RPs) as sequence fingerprints and approaching the localization problem from a regression perspective. Real-world data in an indoor environment are used to build the system, and fairly evaluate it before and after the introduction of an attention module. Our findings show that the architecture involving attention mechanism clearly outperforms the initial RP-based CNN, yet results in much higher prediction latency.
Identifying Influential Nodes in Opportunistic Networks by Network Embedding

In order to accurately identifying influential nodes in opportunistic networks, a novel identifying influential nodes method based on network embedding is proposed. Opportunistic network is sliced into opportunistic network units which is modeled by aggregate graph to present network information. The network embedding model is employed to extract the temporal and structural information among the opportunistic network units, so as to obtain the dynamic attribute features of each node in the network. Taking advantages of the network embedding model's ability of extracting the features of graph data, the mapping relationship between node dynamic attribute features and the node importance is established, so that the influential nodes in opportunistic network can be identified. The experiments results on two real trace datasets show that the proposed method can effectively identifying influential nodes in opportunistic networks, and influential nodes has a better dissemination in the SIR model and NDCG@10 values.
Automatic generation of realistic training data for learning parallel-jaw grasping from synthetic stereo images

This paper proposes a novel approach to automatically generate labeled training data for predicting parallel-jaw grasps from stereo-matched depth images. We generate realistic depth images using Semi-Global Matching to compute disparity maps from synthetic data, which allows producing images that mimic the typical artifacts from real stereo matching in our data, thus reducing the gap from simulation to real execution. Our pipeline automatically generates grasp annotations for single or multiple objects on the synthetically rendered scenes, avoiding any manual image pre-processing steps such as inpainting or denoising. The labeled data is then used to train a CNN-model that predicts parallel-jaw grasps, even in scenarios with large amount of unknown depth values. We further show that scene properties such as the presence of obstacles (a bin, for instance) can be added to our pipeline, and the training process results in grasp prediction success rates of up to 90%.
Enhancing Recommendation Diversity Using Determinantal Point Process Forward Inference and Backward Elimination

Top-N recommendation refers to mining a few specific items that are supposed to be most appealing to the user. While relevancy has been the prevailing issue of the recommendation problem for the last decades, diversity, which is associated with increasing user satisfaction with the presented recommendation lists and mitigating the overfitting problem, also plays a central role in the success of predictive models. Existing work applied determinantal point processes (DPP) to provide a favorable trade-off between relevance and diversity. However, the maximum a posteriori (MAP) inference for DPP is generally NP-hard. To attain an approximate solution with sufficient accuracy, popular approximation approaches such as forward and backward greedy algorithms are used. Despite their intuitive manner, they are not adequate and still be too computationally expensive to be used in large-scale domains. Thus, this paper aims to enhance forward greedy algorithms incorporating backward elimination algorithms and accelerate the greedy MAP inference for DPP by introducing the Cholesky decomposition and Givens rotation. Experimental results show that our proposed algorithm is faster than most competitors and ensures a substantial improvement over the accuracy-diversity trade-off on the Netflix Prize dataset.
Cost Efficient Audio Spectrum Analyzer: Design and Study

Audio spectrum analyzer was developed and researched. The device provides smoothing of the noise, combining left and right channels of the input audio signal, manipulating the input signal to bring to a view that can be processed by a microcontroller, analyzing the signal on the microcontroller and transmitting the frequency spectrum to the driver circuit for visualization. The work of the analyzer was modeled, the algorithm of its functioning was offered and fast Hartley transform with use of various frequencies was investigated.
Trajectory planning using Mixed Reality: an experimental validation

Trajectory planning is at the very core of robotics. Nonetheless, it is not trivial to plan trajectories for industrial or cooperative robots in an intuitive way by non roboticists. Visualizing in 3D a trajectory consisting of a sequence of positions of the end-effector and its orientations is difficult, and becomes even more challenging when kinematics and dynamics constraints should be taken into account. In this work we propose a novel approach, exploiting Mixed Reality, to improve and make easier the planning phase by allowing the user to 'dropping’ way points and leaving to the system the computation of the trajectory, its visualization in augmented reality and, finally, send it to the real robot controller. The method has been experimentally validated on a Panda robot by Franka Emika.
Comparison of Image Segmentation using Different Color Spaces

An image is worth thousands words” this is totally true as the scene is captured by images without loss of perceptual information. And for any scene, colors are indispensable components. Thus for better display of color face images a comparative study between different color spaces was carried out. The study involves three color spaces. Our purpose is to identify the best color space, if there is any, for color image segmentation process.
Localization of Acoustic Gas Leakage Sources with a Circular Microphone Array

In this article, a direction of arrival (DOA) based estimation method is proposed for the localization of acoustic gas leakage sources indoors, with the use of a microphone array. Since the narrowband signal assumption for the uniform circular array (UCA)-MUSIC and ESPRIT methods are not applicable to the case of gas leakage sources, different approaches including the incoherent, coherent, and frequency-domain frequency-invariant beamformer ones are leveraged and optimized for the signal shape considered. The proposed method is evaluated with real-world experimental results obtained with a gas pistol resembling a leakage. The evaluation using experimental data demonstrates that coherent subspace (CSS)-MUSIC is ahead of other methods in the error metrics.
A Novel Deep Learning Architecture for Data-Driven Energy Efficiency Management (D2EEM) - Systematic Survey

The Energy Management System (EMS) is the cost-effectiveness, robustness, and flexible approach for energy efficiency management (EEM). Data-Driven Energy Efficiency Management (D2EEM) is a recent advancement in EMS. The D2EEM is the blend of data science and artificial intelligence for EEM. Due to the highly tolerant to the performance plateau and unconstraint to the feature extraction, Deep Learning (DL) facilitates handling big data-driven problems of EEM. To the best of the knowledge, the accurate and robust D2EEM is the pressing need. Moreover, the accurate pre-trained DL network for EEM is not available in the recent literature. In this work, a comprehensive study is presented to devise a D2EEM. Moreover, the architecture is suggested in connection to the research gap.
Normalizing the brain connectome for communication through synchronization

Networks in neuroscience determine how brain function unfolds, and their perturbations lead to psychiatric disorders and brain disease. Brain networks are characterized by their connectomes, which comprise the totality of all connections, and are commonly described by graph theory. This approach is deeply rooted in a particle view of information processing, based on the quantification of informational bits such as firing rates. Oscillations and brain rhythms demand, however, a wave perspective of information processing based on synchronization. We extend traditional graph theory to a dual particle-wave-perspective, integrate time delays due to finite transmission speeds and derive a normalization of the connectome. When applied to the data base of the Human Connectome project, we explain the emergence of frequency-specific network cores including the visual and default mode networks. These findings are robust across human subjects (N=100) and are a fundamental network property within the wave picture. The normalized connectome comprises the particle view in the limit of infinite transmission speeds and opens the applicability of graph theory to a wide range of novel network phenomena, including physiological and pathological brain rhythms. These two perspectives are orthogonal, but not incommensurable, when understood within the novel here proposed generalized framework of structural connectivity. AUTHOR SUMMARY All networks are composed of nodes and links, forming the structural frame, in which communication occurs. We demonstrate that graph theoretical tools make the implicit assumption of information transmission via exchange of bits, suggesting that the stronger connected nodes are more impactful upon the remainder of the network. This corollary does not extend to communication through oscillations, which is the prominent information carrier in brain networks. We extend traditional network analysis to the oscillatory domain and derive a novel network normalization including descriptive metrics. Along the prototypical example of the brain as a network, we illustrate the consequences of this novel approach and demonstrate that the normalization robustly explains the emergence of the prominent frequency-specific network cores, which cannot be understood within the traditional framework.
Calculation method of orifice area of spool valve with notches

Aimed at two typical notches of U-notch and V-notch in spool valve with notches and based on the configurational feature of the notches and the flow pattern in it,a principle to calculate the orifice area of the two above-mentioned notches was proposed,which the orifice area of U-notch is determined by resistance effect of two stage orifices with two throttling cross-section in series and that of V-notch is specified by the minimum section along the notch pass.The correspondence calculation formulas to calculate the orifice area of typical notches was obtained,then the orifice area of spool valve with notches can be got using the superposition principle.A general computational program to calculate the orifice area of spool valve with notches is developed.The investigation results showed that this method possessed clear and definite physical meaning and high accuracy,realizing the programmed computation of the area of complex orifice in spool valve.
Characteristics of Suspended and Mobile Microbubble Partial Discharge in Insulation Oil

In order to study the partial discharge (PD) characteristics of suspended and mobile microbubble in insulation oil,the deformation mechanism of microbubbles in different motion state was discussed from the viewpoint of mechanics.The deformation parameter c of microbubble was defined using mathematical ellipse parameters,the surrounding electric field distribution with the characteristic parameter c changes was analyzed,and the mode of stimulating the suspend and mobile microbubbles in transformer oil was designed.Methods of ultra-high frequency and pulsed current were used to test the partial discharge of mobile and stationary microbubble,the time-frequency characteristics of different discharge were analyzed,and three-dimensional map was constructed.The results show that with the oil flow rate increases,the ellipsoidal microbubble changes from the horizontal stretch to a vertical stretch,the electric field distribution of microbubbles between the pole decreases,and the partial discharge inception voltage is increased.Therefore,increased flow will help to improve the oil dielectric strength.
Numerical simulation of dynamic response of overlap tunnels in close proximity due to train's vibrating load

Based on track vibrating acceleration data measured from tunnels of Shanghai Metro Project, the numerical simulations by using 3D and 2D FEM are carried out in order to understand the dynamic response of overlap tunnels in close proximity to Nanpu Bridge Project due to the train抯 vibrating load. The follwing conclusions are given: 1.mutual influences between the overlap tunnels are much greater than the parallel ones; 2. the longer the distance between the tunnels, the less the mutual influences; 3. for the tunnel structure design, the static soil pressure is the key load, and the additional stress induced by the train抯 vibrating load is much smaller than the stress induced by the static load.
Simulation on Bending Performances of Ancient Superposition Beam with Decay Components

It was found by survey that the decay occured on a superposition beam(composed of purlin,tie plate and tie beam) of Yinghua Palace in the Forbidden City.To effectively protect the decayed superposition beam,its bending performances were studied by simulation.Based on the decay status of purlin and tie beam parts,the finite element model of remain parts of the superposition beam was built considering the tenon-mortise connection type of boundary conditions.By static analysis,the displacement,bending as well as shear strength of the decayed superposition beam under vertical loads were obtained,and the feasible suggestions for strengthening were proposed.The results show that the decayed superposition beam still has enough bending strength,and its maximum displacement value is still within permission scope.Besides,so far as shearing strength,the remain part of tie beam is still strong enough,while the remain purlin is not so strong because of its insufficiency section sizes.Thus the different strengthening methods have to be considered and adopted for the decayed purlin and tie beam to mitigate the destruction of the superposition beam.
Yapılandırmacı Öğrenme Yaklaşımının Öğrenenlerin Üstbiliş Farkındalıklarına Katkısı

Bu calismanin amaci, yapilandirmaci ogrenme yaklasiminin sayiltilarina uygun denencel bir program tasarisi hazirlayarak uygulamasini gerceklestirmek ve bu tasariyi, geleneksel yaklasima gore ogrenenlerin ustbilis farkindaliklarina katkilari acisindan degerlendirmektir. Arastirmada ontest-sontest kontrol gruplu deneme modeli ile nitel veri birlesiminden olusan karma arastirma modeli kullanilmistir. Arastirma, altinci sinif Sosyal Bilgiler dersinde yurutulmustur. Nitel veriler; ogrenenlerin yansitici gunluklerinden - ustbilissel dusunme kayitlarindan, yapilandirilmis gozlem formuyla saglanan alan notlarindan ve katilimcilarla yapilan gorusmelerden saglanmistir. Arastirmacilar tarafindan gelistirilen besli Likert tipi Ustbilis Farkindalik Olcegi (UFO), ogrenenlerin ustbilis farkindaliklarini test etmek icin kullanilmistir. Arastirmanin nicel alt problemlerini analiz etmek icin “t” testi kullanilmistir. Nitel veri seti, icerik analizi kullanilarak tumevarimci bir yaklasimla arastirmacilarin bu calisma icin gelistirdigi ozgun bir stratejiyle cozumlenmistir. Arastirmada elde edilen sonuclar yapilandirmaci program tasarisina yonelik uygulamalarin; ogrenenlerin ustbilis farkindaliklarini gelistirmede geleneksel yaklasima gore daha etkili oldugunu gostermistir.
EMI Analysis and EMC Design of Three-phase Multifunctional Harmonic Energy Meter

According to power system requirements,a kind of three-phase multifunctional harmonic energy meter with radio frequency identification(RFID)interface and general packet radio service(GPRS) communication module was developed.The structure and the working principle of the energy meter were described,and its space electromagnetic interference(EMI),conducted coupling interference,and common impedance coupling interference were analyzed.The EMI propagation paths of energy meter were discussed,and the reliability design scheme of energy meter was proposed.The electromagnetic compatibility(EMC)design method of energy meter such as EMC of switch power,PCB routing,communication interface anti-interference design,the anti-static design and software anti-interference design,were analyzed.The EMC test results of the energy meter were given.The experiment and operating results have shown that the three-phase multifunctional harmonic energy meter has better EMC.
Detection of LDDoS Attack Based on Kalman Filtering

LDDoS(Low-rate Distributed Denial of Service) attack is a new class of DDoS,which exploits TCP's RTO(Retransmission Time Out) mechanism.An LDDoS attack can elude the monitor of traditional detection approach by sending low-rate packets in the way of periodic pulse to a victim.This paper proposes an approach of detecting LDDoS attack based on kalman filter.The error between one step prediction and the optimal estimation is used as the detection criterion.Experiments in simulation environment and practical network are conducted to test the detect performance and about 89.6% detect probability is achieved.Results show that this approach has an expected effect in detecting LDDoS attack.
of School-Based Journal of School-Based Counseling Policy and Evaluation Counseling Policy and Evaluation

The ongoing need for clinical supervision of practicing school counselors in the United States has reached a critical point. Given the acute mental health needs of children and adolescents, staggering caseloads, and pandemic repercussions, school counselors often receive insufficient clinical support. Clinical supervision is necessary to safeguard against burnout, promote ethical practice, and encourage continual skill development for school counselors worldwide. Unfortunately, there remains a lack of available clinical supervision, trained supervisors, and practice-based strategies for providing consistent quality supervision for post-master’s school counselors. The authors present a rationale for peer clinical supervision of practicing school counselors in the United States and globally. Recommendations are provided for practitioners and stakeholders to be actively involved in strengthening the professional practice of school counselors worldwide through peer clinical supervision.
Generating intuitive behaviour in artificial intelligence

— Intuition is a sub-conscious but highly effective decision-making process that makes us questions and increase our intelligence. It is generated on the emotional/creative side of our brain and is something that have been proven to be nonexistent for machines. A stimulation is also presented in this study that will help develop an algorithm that mimic the human intuition process. The main question focused in this study is that can a machine become able to figure out the unknown. This study concludes with the potential areas of where this algorithm can be implemented or what would be the ideal application that can use the intuitive artificial intelligence to answer the unknown.
Predicting iron ore products from core samples: integration of extended fields image analysis, automated mineralogy, and XRD/Rietveld method

A preliminary characterisation of the iron ore assets of Atlantica Geologia e Mineração SA (AGEMISA) Campo Grande Project was performed on a sample composed from drill cores, to evaluate possible products and processing requirements. Magnetite largely predominates over hematite, and major gange minerals are quartz, amphibole and feldspar. Liberation spectra starting at 1⁄4” indicate there is no liberation above 1 mm, therefore no lump ore can be expected. For the sinter feed fraction (850x150 μm) an overall magnetite+hematite recovery close to 90% can be attained for a similar grade of the minerals, corresponding to 61% of Fe. The liberation is excellent for the pellet feed size range, granting recoveries better than 99% for 96% iron oxides, or a 66% Fe grade. The results are comparable to heavy liquid concentrates.
Recommender systems enhancement using deep reinforcement learning

— Recommendation systems provide users with personalized recommendations, stand out as systems aiming to provide appropriate and efficient services. Traditional recommendation systems provide suggestions to their users with static approaches and do not include user preferences that change over time in suggestion strategies. In this study, a comprehensive review and comparison of recommendation systems that can adaptively develop suggestion approaches according to changing user preferences and learn user preferences is presented. In this research other approaches and solutions with frameworks of Deep Reinforcement Learning will be compared. In all articles that is reviewed Markov Decision Process (MDP) were used as a solution for dynamic recommendations and long-term rewards for the users. Proposed frameworks like DEERS, DRR etc. will be reviewed.