Miracle-dz/UniFAR-FTUs huggingface dataset:
Iteration Time Prediction for CNN in Multi-GPU Platform: Modeling and Analysis

Neural networks, as powerful models for many difficult learning tasks, have created an increasingly heavy computational burden. More and more researchers focus on how to optimize the training time, and one of the difficulties is to establish a general iteration time prediction model. However, the existing models have high complexity or tedious build processes, and there is still space for improvement in prediction accuracy. Moreover, there is little systematic analysis of multi-GPU which is a special and widely used scenario. In this paper, we introduce a framework to analyze the training time for convolutional neural networks (CNNs) on multi-GPU platforms. Based on the analysis of GPU calculation principles and its special transmission mode, our framework decomposes the model and obtain accurate prediction results without long-term training or complex data collection. We start by extracting key feature parameters related to GPUs, CNNs, and networks. Then, we map CNN architectures to constraints, including software platforms, GPU platforms, parallel strategies, and communication strategies. At last, we provide the prediction model and give analysis results of training time from multiple perspectives. The proposed model is verified on four types of NVIDIA GPU platforms and six different CNN architectures. The experiment results show that the average error across varies scenarios is less than 15% and outperform the state-of-the-art results by 5%–30%, which corroborate our model an effective tool for artificial intelligence (AI) researchers.
Optimal Size and Location of Piezoelectric Actuator/Sensors: Practical Considerations

In this paper, we address the problem of obtaining the optimal size and location of piezoelectric actuator/sensors. An optimization problem is formulated for a general beam which has arbitrary boundary conditions and may have as many piezoelectric actuators as desired. The proposed optimization criterion is based on the beam modal cost and modal controllability. If the size of the actuator is unbounded, it frequently is optimal if it covers most if not all the length of the beam. This is not realistic, since there are cost, weight, and space factors to be considered. By adding a penalty term to the criterion, the size of the actuator/sensor can be reduced to a practical and reasonable size. Thus there is no need to pre-select the size of the actuator/sensor. The optimal size and location for beams with various boundary conditions are determined for a single pair and for two pairs of actuators. The results in this paper are in very good agreement with those reported by other investigators. A comparison is also made between the performance of two pairs of actuators and the performance of a single pair. The improvement in performance with two pairs is quantified.
Compensation of Process Forces with a Model-Based Feed-Forward Control for Robot Machining

Low investment costs, high flexibility and a wide working area have led to an increasing use of industrial robots (IR) for machining tasks like milling. However, the conventional control algorithms are not designed to cope with large dynamic and static process forces as in milling operations. Therefore, the occurring inaccuracies and the low stiffness of an IR inhibit the breakthrough of industrial robots in the machining sector. This paper presents a model-based control algorithm, which enables the calculation of drive torques in the presence of external forces and therefore optimizes the milling process.
The Research on the Complexity of the Equipment Support Network Based on Complex Networks

A new method for equipment support network was introduced within the framework of complex networks theory, and in that case, the topological structure and formation characteristics of the real support network would be better understood. Especially, several typical statistical properties of the network which was from three different levels, i. e. , unit level, module level and network level were analyzed. The empirical results show that equipment support network has the similar topological attributes, such as scale free behavior, small world effect, community structure and degree correlation comparing with other previously studied social or technical networks.
On-Chip Dispersion Measurement of the Quadratic Electro-Optic Effect in Nonlinear Optical Polymers Using a Photonic Integrated Circuit Technology

A novel method to determine the dispersion of the quadratic electro-optic effect in nonlinear optical materials by using a silicon-on-insulator microring resonator is presented. The microring consists of a silicon slot waveguide enabling large dc electric field strength at low applied voltages. The dispersion of third-order hyperpolarizability of a linear conjugated dye is approximated by using a two-level model for the off-resonant spectral region. As an example, the dispersion of the resonance wavelength of the resonator filled with a dye doped polymer was measured in dependence of the applied dc voltage. The polymer was poly (methylmethacrylate) doped with <inline-formula><tex-math notation="LaTeX">$\text{5 wt}\%$</tex-math></inline-formula> disperse red 1 (DR1), and the measurements have been carried out at the telecommunication wavelength band around <inline-formula><tex-math notation="LaTeX">$\text{1550 nm}$</tex-math></inline-formula> (optical <italic>C</italic>-band). The described measurements represent a new technique to determine the dispersion of the third-order susceptibility and molecular hyperpolarizability of the material filled into the slot of the ring-resonator.
Cyber Security: Threat Detection Model based on Machine learning Algorithm

A threat can be anything that causes potential damage to the network system. These threats can turn out to be a attack to the system. Threat may occur in any forms like viruses, outright attack, and phishing attack from hackers to gain information. Such attacks put a user’s system and also business system at risks. Cyber security aims at the protection of system from attacks like unauthorized network access, intrusions attacks etc. This paper presents a novel architecture model based on machine learning for the prediction of Cyber security malware that requires execution in a sandbox environment. In order to prevent the attackers from infiltrating the system Machine Learning approach is adapted.
On-line minimization of running costs, greenhouse gas emissions and the impact of distributed generation using microgrids on the electrical system

Distributed generation systems composed of non-renewable and renewable power sources is one of the best approaches for reducing greenhouse gas emissions. Nevertheless, uncontrolled integration of power sources in the distribution system may have negative effects on efficiency and working parameters. Global optimization of distributed generation in the system is not available, but microgrids arrangements make it possible to the design management systems which are able to control their working parameters and give fast responses to internal events without affecting the distribution system. This work presents a new active-power dispatch algorithm capable of adjusting microgrid generation to demand on-line in grid-connected-mode. It also reduces the greenhouse gas emissions to a minimum, and optimizes running costs of microsources. The algorithm uses a heuristic approach based on cost functions of microsources and has been tested to solve different power dispatch cost optimization problems. The results obtained are superior to those obtained by applying state-of-the-art optimization methods, in terms of global cost and emissions, system stability, and computational resources requirements. This reduction of the requirements of computational resources makes it possible to run the algorithm on-line, using an off-the-shelf programmable logic controller or microcontroller. Thus, the infrastructure requirements and new investments are reduced and the penetration of microgrids based on renewable energies is improved.
Influence of the Electric Field and Temperature on Space Charge in DC Cable Joints

Space charge behavior in cross-linked polyethylene (XLPE), ethylene-propylene rubber (EPR) and multi-layered XLPE/EPR subjected to electric field and temperature has been investigated using the pulsed electro acoustic (PEA) technique. A dual-layer structure constituted of two flat sheets, where one is XLPE and the other is EPR was used to study the charging properties of the electrode/dielectric and dielectric/dielectric interfaces. The time dependence of the space charge distribution was recorded at different temperatures and fields and short circuit condition. The results show that the increase of the electric field leads to an increase of the space charge density values, and the increase of temperature leads to an increase of the space charge density in the entire sample, excepting the area near the ground electrode.
Analytical Modelling for nanoscale Gate Engineered Silicon-On-Nothing MOSFET with High-K dielectric

In this paper, a two-dimensional analytical model of nanoscale Gate Engineered Silicon-On-Nothing (SON) MOSFET with High-K dielectric underneath the gate has been introduced. The parabolic approximation method for solving 2-D Poisson’s equation with suitable boundary conditions has been employed for determining the potential distribution function of the device. The performance comparison of High-K Gate engineered SON MOSFET Low-K dielectric has been investigated to establish superiority of the proposed structure in terms increased immunity against short channel effects (SCE). The proposed model has been validated by the close agreement between the calculated and simulated values obtained from an ATLAS simulator.
Rough Set Processing Outliers in Cluster Analysis

Cluster analysis is a very important data mining technology, and also a hot issue in data mining research. Among many data types to be clustered, mixed attribute data is the most common one. Among them, the categorized attribute values are limited, disordered and can’t be compared in size, which makes it difficult to determine the similarity measure that can reasonably describe the differences between sample objects. In addition, it is often impossible to convert class attributes into numerical attributes. At present, many numerical clustering algorithms are not suitable for processing mixed attribute data, but the number of algorithms that can process such data is small, and the performance and clustering quality still need to be improved.
g-MODE EXCITATION DURING THE PRE-EXPLOSIVE SIMMERING OF TYPE Ia SUPERNOVAE

Prior to the explosive burning of a white dwarf (WD) that makes a Type Ia supernova (SN Ia), the star “simmers” for ∼103 yr in a convecting, carbon-burning region. I estimate the excitation of g-modes by convection during this phase and explore their possible effect on the WD. As these modes propagate from the core of the WD toward its surface, their amplitudes grow with decreasing density. Once the modes reach nonlinear amplitudes, they break and deposit their energy into a shell of mass ∼10−4 M☉. This raises the surface temperature by ≈4 × 108 K, which is sufficient to ignite a layer of helium, as is expected to exist for some SN Ia scenarios. This predominantly synthesizes 40Ca, but some amount of 28Si, 32S, and 44Ti may also be present. These ashes are expanded out with the subsequent explosion up to velocities of ∼20, 000 km s−1, which may explain the high velocity features (HVFs) seen in many SNe Ia. The appearance of HVFs would therefore be a useful discriminant for determining between progenitors, since a flammable helium-rich layer will not be present for accretion from a C/O WD as in a merger scenario. I also discuss the implications of 44Ti production.
Loco‐regional treatment of hepatocellular carcinoma

Loco‐regional treatments play a key role in the management of hepatocellular carcinoma (HCC). Image‐guided tumor ablation is recommended in patients with early‐stage HCC when surgical options are precluded. Radiofrequency ablation has shown superior anticancer effects and greater survival benefit with respect to the seminal percutaneous technique, ethanol injection, in meta‐analyses of randomized controlled trials, and is currently established as the standard method for local tumor treatment. Novel thermal and nonthermal techniques for tumor ablation—including microwave ablation, irreversible electroporation, and light‐activated drug therapy—seem to have potential to overcome the limitations of radiofrequency ablation and warrant further clinical investigation. Transcatheter arterial chemoembolization (TACE) is the standard of care for patients with asymptomatic, noninvasive multinodular tumors at the intermediate stage. The recent introduction of embolic microspheres that have the ability to release the drug in a controlled and sustained fashion has been shown to significantly increase safety and efficacy of TACE with respect to conventional, lipiodol‐based regimens. The available data for radioembolization with yttrium‐90 suggests that this is a potential new option for patients with HCC, which should be investigated in the setting of randomized controlled trials. Despite the advances and refinements in loco‐regional approaches, the long‐term survival outcomes of patients managed with interventional techniques are not fully satisfactory, mainly because of the high rates of tumor recurrence. The recent addition of molecular targeted drugs with antiangiogenic and antiproliferative properties to the therapeutic armamentarium for HCC has prompted the design of clinical trials aimed at investigating the synergies between loco‐regional and systemic treatments. The outcomes of these trials are eagerly awaited, because they have the potential to revolutionize the treatment of HCC. (HEPATOLOGY 2010;)
The Effects of Multi-Sensory Augmented Reality on Students’ Motivation in English Language Learning

English as Second Language (ESL) learning widely implemented in the global education system as English is essential for workplace, diplomate communication, and contractual matters. The effective teaching and learning of ESL since early age provide the students competitive advantages. Deployment of multimedia such as Augmented Reality (AR) in ESL had been implemented for this purpose. This study used multimedia learning environment AR English Vocabulary Acquisition (ARenVA) to test its effectiveness of learning among 37 Malaysia primary school students from 4th, 5th, and 6th grade. Explanatory research was conducted with instruments namely visual text and AR 2D (ARvt), spoken text and AR 2D (ARst), visual text, spoken text and AR 2D (ARvtst). Interview, observation, pre-test/post-test questionnaire and English test were used to identify students’ perceived motivation and English performance. The perceived motivation is in relation with Keller’s ARCS model of Attention, Relevance, Confidence and Satisfaction. SPSS was used to analyze the quantitative data; while NVivo and content analysis were utilized to analyze the qualitative datasets of semi-structured interview. Findings indicated that the ARenVA’s ARstvt treatment mode significantly improves the vocabulary acquisition of ESL learning by 30.76% and perceived motivation by 11.50%; more effective in motivating students by 32 times as compared to ARvt treatment mode. The novelty of this study situated on the effects of AR English learning towards improving motivation and English vocabulary acquisition of ESL learning among 4th, 5th and 6th grade Malaysia student, significantly identified its benefit through the numerical percentages. Nevertheless, the instructional design of AR English learning controls the success of learning results. The paper reviews on the success systematic implementation of different ARenVA treatment modes.
Probabilistic RSS Fingerprinting for Localization in Smart Platforms

Location-Based Services (LBS) are the major services offered by IoT platforms. Localization techniques are the key to these services. This is possible because of modern electronic sensors with radio interface are embedded ubiquitously in IoT platform. Localization techniques analyzes the radio signal fluctuation of these sensors to build a pattern called signal fingerprint. The localization methods can be device based active localization or device-free passive localization. Passive localization method need not place sensor to the target device in contrast to the active method where each target must have a sensor for localization. It estimates the changes in the radio signal pattern to find the target location.These methods are applicable in traffic surveillance, pervasive computing and smart environment. This approach represents the signal parameters to generate the map. Further the new signal from an unknown object is matched with the map to find the location. The device free localization system performance depends on the radio signal model. This paper proposes a probabilistic radio signal model for map generation and localization. Moreover, this paper compares this probabilistic approach with existing KNN approach using UJIIndoorLoc dataset.
Testing the quantity-quality fertility model: the use of twins as a natural experiment.

The predictive content of the quantity-quality model of fertility and the empirical information required for verification under a minimal set of restrictions on the utility function is described. It is demonstrated that commodity-independent compensated price effects must be known to infer the existence of the unobservable interdependent shadow prices of the model with a relatively weak structure improsed on preference orderings. A method of using multiple birth events to substitute for these exogenous prices is proposed and applied to household data from India.
Poetic Meter Classification Using Acoustic Cues

Poems, which communicate through rhythm and its apparent meaning have a vital role in any literary. Meter, a set of well defined rules gives rhythm to the poetry. In this paper, a meter classification scheme using fusion of low level, mid-level and high-level musical texture features, computed from recited poems is addressed. The performance of the proposed system is evaluated using a newly created poetic corpus in Malayalam, one of the classical languages in India. Initially, a baseline system with mel-frequency cepstral coefficient (MFCC) feature set is performed. In the second phase, experiment is conducted with musical texture features. In the third phase, experiment is extended using early fusion of MFCC with the feature set considered in the second phase. Support vector based classifier is used in the classification phase. Later, the same feature-sets are experimented with deep neural network(DNN) based classifier. Whilst MFCC-SVM system reports an overall accuracy of 60%, the second phase reported an accuracy of 68%. In the third phase, complementary information provided by the MFCC and musical texture features aided to improve the system performance (accuracy, 90%). In the DNN based experiments, the highest accuracy of 92% is reported for feature-fusion. The experimental study shows the promise of early fusion of MFCC with musical texture feature set in poetic meter classification and its analysis.
Unsupervised Learning Approach-Based New Optimization K-Means Clustering for Finger Vein Image Localization

Today, finger vein recognition attracts a lot of attention as a promising approach of biometric identification framework. It still a big challenge since it is not capable to localize and extract finger vein, since most of the datasets have a lot of noise and redundant unwanted object in the background. Image thresholding it seems to be a suitable approach for the binarization stage that is utilized into some exact finger vein image detection and extraction. In this paper, a new optimization methodology was proposed based on an unsupervised learning framework for the purpose of designing a new clustering algorithm that replaces the distance criteria into intensity level majority for group similar image pixels in one cluster. The propose system for finger vein image localization based optimized clustering algorithm has many stages such as using some significant pre-processing steps to enhance the finger vein images such as Wiener filtering, image smoothing and image sharpening for edge detection. Cluster stabilization, cluster random initialization, optimized objective function are the most significant points that have been solved. One of the most challenges dataset is that the finger vein images are not biased, and the images have more background noise and details. The experimental results of the finger vein localization based on an optimized clustering algorithm show that it much faster than the other clustering algorithms such as k-means, FCM by consuming an average time (2.83 sec.) while the k-means and FCM consumed (26.27sec.), and (80.36 sec.) respectively.
Investigation of Particle Initiated Partial Discharges in Transformer Oil with Pressboard Barrier Under Divergent Fields

Particle initiated partial discharge (PD) in oil is one of the significant problem associated with transformer. Among particles, conductive particles play very detrimental role. The existence of conducting particles is very difficult to avoid completely during the manufacturing process and also it can be developed during service period of the transformer. These free conductive particles can adhere to paper or pressboard insulation barrier or freely move in the bulk oil. In transformers divergent (non-uniform) fields are often present due to the geometry of the internal parts and may be due to sharp points. Therefore to understand and characterize the effect of conducting particle in oil with pressboard barrier under divergent fields are very critical for the life assessment of transformer. Sphere-plane and needle-plane electrodes are used to generate different non-uniform fields. The discharge measurements are carried out using partial discharge measurement technique according to IEC 60270. Phase resolved partial discharge (PRPD) pattern, discharge characteristics such as average and peak discharge magnitude, number of discharges per cycle, discharge power, rise and duration time of PD pulse due to conducting particle in oil with pressboard barrier under divergent fields are analyzed. The observed PRPD pattern were relatively similar regardless of applied divergent fields. The observed PD inception is much earlier in quasi uniform field compare to non-uniform filed, the PD inception voltage (PDIV) in non-uniform field is three times higher than that occur at quasi-uniform field.
Simulation framework for performance evaluation of passive RFID tag-to-tag communications

The concept of passive RFID tag-to-tag communications has been recently introduced and opens new promising perspectives, especially in the field of Internet-of-Things. In this paper, a simulation framework is proposed as a new tool allowing the performance evaluation of tag-to-tag radio links. The modeling takes into consideration the external source supplying the communication between tags, radiating characteristics of tag antennas, and reception system aspects. Performance results are expressed in terms of Bit Error Rate (BER) with respect to the distance between the tags and the position of the energy source relative to the position of the two tags.