Miracle-dz/UniFAR-FTUs huggingface dataset:
Research on Multi-feature and Machine Learning Hierarchical Pedestrian Detection Method Based on Deep Learning

With the rapid development of science and technology, computers have become an important way for people to obtain information. As an important means of transmitting visual information, image plays a very important role. Pedestrian detection is a key research field of machine vision and a key technology in intelligent video surveillance and intelligent transportation system. Pedestrian detection is the basis and premise of pedestrian tracking, behavior analysis, gait analysis and pedestrian identification, and a good pedestrian detection algorithm can provide strong support and guarantee for the latter. Pedestrian detection is not an independent problem, it is closely related to video pedestrian tracking, human behavior recognition and pedestrian identification. The method of machine learning is to let the program have the ability of learning. Through continuous training and iteration, the parameters of the model are optimized. Deep learning can give full play to the speed and rationality of the machine and improve the accuracy of pedestrian detection.
On Comparison Study between Double Sumudu and Elzaki Linear Transforms Method for Solving Fractional Partial Differential Equations

في هذا البحث، استخدمنا طريقتي التحويلان التكامليان المضاعفان سمودو و الزاكي للحصول على الحلول العددية لبعض انواع المعادلات التفاضلية الجزئية الكسرية ذات المعاملات الثابتة، ووضحنا كفاءة الطريقة بتقديم بعض الامثلة العددية المحسوبة باستخدام برنامجي ماثكاد 15 و ماتلاب R2015a
Comparison of SARIMA and SVM model for rainfall forecasting in Bogor city, Indonesia

Heavy rainfall and flash floods are still significant for several cities in Indonesia, especially Bogor City. Rainfall prediction is essential to develop early warning systems for early detection of massive and flash floods. The accurate forecasting method is needed to produce accurate predictions of rainfall. Several methods have been developed to get a precise rainfall forecast, such as the classical approach, such as seasonal autoregressive integrated moving average (SARIMA), and machine learning approach, for example, gradient boosting and support vector machine (SVM). In this study, we introduce for forecasting rainfall at Bogor city, Indonesia. The SVM provides an accurate result with a small MAPE.
Investigation of training data size for real-time neural vocoders on CPUs

Keisuke Matsubara ,, Takuma Okamoto, Ryoichi Takashima, Tetsuya Takiguchi, Tomoki Toda, Yoshinori Shiga and Hisashi Kawai Graduate School of System Informatics, Kobe University, 1–1 Rokkodaicho, Nada-ku, Kobe, 657–8501 Japan National Institute of Information and Communications Technology, 3–5 Hikaridai, Seika-cho, Soraku-gun, Kyoto, 619–0289 Japan Information Technology Center, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, 464–8601 Japan
Biological synthesis of Nanoparticles from Medicinal Plants: Recent Studies

In a recent trend, nanotechnology has been developed to be an important field of research in various fields like medicinal chemistry, pharmaceuticals and all. On the other hand, compared to chemical method of synthesis, method of synthesis of nanoparticles by green method is simple, effective & ecofriendly. Plant extracts are obtained from the plant, creep, cereals, etc. generally natural extracts are prepared by decoction method or solid powder formation method. The natural extracts are very much effective in the medicinal approach. The study of Nano sized particles towards natural extracts has given a novel way in the field of medicines. So from the nanomaterials of natural extracts, one can expect still good effective results. As we know that preparation of nanoparticles through green approach is one of the good selections in the view of betterment of ecology. Therefore by taking aqueous extracts of medicinal plants’ parts and metal ions, nanoparticles can be synthesized. The plant extract and metal ions are taken in a fixed ratio and keep at room temperature for the reduction. The Changes in the Color confirmed the formation of nanoparticles. Further, the synthesized nanoparticles were characterized by UV, EPMA, XRD and FTIR data.
Parallel news clustering and topic modeling approaches

At the current age there is an urgent need in developing massively scalable and efficient tools to Big Data processing. Even the smallest companies nowadays inevitably require more and more resources for data processing routines that could enhance decision making and reliably predict and simulate different scenarios. In the current paper we present our combined work on different massively scalable approaches for the task of clustering and topic modeling of the dataset, collected by crawling Kazakhstan news websites. In particular, we propose Apache Spark parallel solutions to news clustering and topic modeling problems and, additionally, we describe results of implementing document clustering using developed partitioned global address space Mapreduce system. In our work we describe our experience in solving these problems and investigate the efficiency and scalability of the proposed solutions.
A new compact model for bipolar RRAMs based on truncated-cone conductive filaments—a Verilog-A approach

A new model for bipolar resistive random-access memories (RRAMs) is presented in this article. Redox and diffusion processes are used to describe in detail the physics behind the filamentary resistive switching (RS) mechanisms of the RRAMs under study. The model includes truncated-cone shaped filaments which are known to be close to the real conductive filament (CF) geometry and a detailed thermal approach, where two temperatures are considered to describe the rupture process at the CF’s narrowest part and also the main CF body’s electrical conductivity variations. Ti/ZrO2/Pt RRAM devices have been fabricated and measured, and the model has allowed us to reproduce the experimental data for all the cases analyzed. Finally, the model has been implemented in Verilog-A code within the ADS circuit simulator, and the response of a device to pulsed external voltages within a characterization circuit has been simulated, producing good results when compared with experimental measurements.
Infrared Action Detection in the Dark via Cross-Stream Attention Mechanism

Action detection plays an important role in video understanding and attracts considerable attention in the last decade. However, current action detection methods are mainly based on visible videos, and few of them consider scenes with low-light, where actions are difficult to be detected by existing methods, or even by human eyes. Compared with visible videos, infrared videos are more suitable for the dark environment and resistant to background clutter. In this paper, we investigate the temporal action detection problem in the dark by using infrared videos, which is, to the best of our knowledge, the first attempt in the action detection community. Our model takes the whole video as input, a Flow Estimation Network (FEN) is employed to generate the optical flow for infrared data, and it is optimized with the whole network to obtain action-related motion representations. After feature extraction, the infrared stream and flow stream are fed into a Selective Cross-stream Attention (SCA) module to narrow the performance gap between infrared and visible videos. The SCA emphasizes informative snippets and focuses on the more discriminative stream automatically. Then we adopt a snippet-level classifier to obtain action scores for all snippets and link continuous snippets into final detections. All these modules are trained in an end-to-end manner. We collect an Infrared action Detection (InfDet) dataset obtained in the dark and conduct extensive experiments to verify the effectiveness of the proposed method. Experimental results show that our proposed method surpasses state-of-the-art temporal action detection methods designed for visible videos, and it also achieves the best performance compared with other infrared action recognition methods on both InfAR and Infrared-Visible datasets.
Age, growth, and length–weight relationship of the checkered snapper Lutjanus decussatus in an Okinawan coral reef

Species in the family Lutjanidae (snappers) are important targets of fisheries around the world. Although the checkered snapper Lutjanus decussatus is distributed across the Indo-Pacific, little is known about the demographic parameters of this species. Thus, the age, growth, and length–weight relationship of the checkered snapper L. decussatus (family Lutjanidae) were investi-gated in an Okinawan coral reef. Alternate translucent and opaque zones were observed in all sectioned otoliths, with an opaque zone occurring at the edge of the otolith predominantly during the spawning season. The maximum ages of the male and female were 24 and 23 years, respectively. The von Bertalanffy growth parameters for the relationship between age and fork length (FL) were as follows: asymptotic mean FL ( L ∞ ) ＝ 288.3 mm, growth coefficient ( k ) ＝ 0.09 year － 1 , and age when mean FL is 0 ( t 0 ) ＝－ 12.0 years in males; L ∞ ＝ 316.5 mm, k ＝ 0.08 year － 1 , and t 0 ＝－ 12.1 year in females. In the case that the growth parameters were re-estimated using a fixed t 0 value of 0, L ∞ ＝ 256.1 mm, and k ＝ 0.54 year － 1 in males; L ∞ ＝ 274.4 mm, and k ＝ 0.45 year － 1 in females. L ∞ was significantly greater in females than in males, but there were no significant inter-sexual differences in K or t 0 . The length–weight relationships for males and females were whole body weight (g) ＝ 4.95 × 10 － 5 FL 2.83 and 1.44 × 10 － 5 FL 3.05 , respectively. The present study is the first to demonstrate the age, growth, and length–weight relationship of L. decussatus, which would be useful biological information for the effective management of this species.
Study of Pani Nanofibers Supported Pt Catalysts for Fuel Cell

In this study, the preparation and characterization of polyaniline nanofibers supported platinum (Pt) as novel cathode catalyst for Proton exchange membrane fuel cells (PEMFCs) are presented. This paper aims to study the production of polyaniline nanofibers (PANI-NFs) at different aniline/oxidant ratios via interfacial polymerization method. PANI-NFs supported Pt catalysts (Pt/PANI-NFs) were synthesized by an ethylene glycol reduction method. The synthesized PANI support material and Pt/PANI catalyst are analyzed using several methods on Scanning Electron Microscopy with LXRF system (SEM), X-ray diffraction (XRD) and Fourier Transform Infrared Spectroscopy (FT-IR).
A Review of Scalability Issues in Software-Defined Exchange Point (SDX) Approaches: State-of-the-Art

Internet Exchange Points (IXPs) interconnect heterogeneous networks and transfer substantial traffic volumes. In the past decade, the number of IXPs has seen tremendous growth, with more operators connecting to these IXPs even though these IXPs faced various inter-domain routing limitations. Routers based on Border Gateway Protocol (BGP) forwards packets only based on destination IP prefix and selects only routes learned from their neighbors. IXPs designed using Software-Defined Network (SDN), called SDX, offer solutions for existing inter-domain routing problems. This paper presents the existing scalability limitations of inter-domain routing at IXP and how traditional IXP structural design can be transformed into a highly scalable SDX design by exploiting the SDN platform’s functionalities in different use cases of SDX. The paper then reviewed how the SDX improved various IXP operators’ scalability by reviewing and analyzing the latest SDX models and approaches, which provide enhanced policies to enhance providers’ management operations and offer good quality of services (QoS) to the various participating members. Finally, we discussed the open issues and challenges in this area that need further study and a solution to tackle them.
Event endings in memory and language

ABSTRACT Memory is fundamental for comprehending and segmenting the flow of activity around us into units called “events”. Here, we investigate the effect of the movement dynamics of actions (ceased, ongoing) and the inner structure of events (with or without object-state change) on people's event memory. Furthermore, we investigate how describing events, and the meaning and form of verb predicates used (denoting a culmination moment, or not, in single verbs or verb-satellite constructions), affects event memory. Before taking a surprise recognition task, Spanish and Mandarin speakers (who lexicalise culmination in different verb predicate forms) watched short videos of events, either in a non-verbal (probe-recognition) or a verbal experiment (event description). Results show that culminated events (i.e. ceased change-of-state events) were remembered best across experiments. Language use showed to enhance memory overall. Further, the form of the verb predicates used for denoting culmination had a moderate effect on memory.
Effects of Climatic Drivers and Teleconnections on Late 20th Century Trends in Spring Freshet of Four Major Arctic-Draining Rivers

Spring freshet is the dominant annual discharge event in all major Arctic draining rivers with large contributions to freshwater inflow to the Arctic Ocean. Research has shown that the total freshwater influx to the Arctic Ocean has been increasing, while at the same time, the rate of change in the Arctic climate is significantly higher than in other parts of the globe. This study assesses the large-scale atmospheric and surface climatic conditions affecting the magnitude, timing and regional variability of the spring freshets by analyzing historic daily discharges from sub-basins within the four largest Arctic-draining watersheds (Mackenzie, Ob, Lena and Yenisei). Results reveal that climatic variations closely match the observed regional trends of increasing cold-season flows and earlier freshets. Flow regulation appears to suppress the effects of climatic drivers on freshet volume but does not have a significant impact on peak freshet magnitude or timing measures. Spring freshet characteristics are also influenced by El Niño-Southern Oscillation, the Pacific Decadal Oscillation, the Arctic Oscillation and the North Atlantic Oscillation, particularly in their positive phases. The majority of significant relationships are found in unregulated stations. This study provides a key insight into the climatic drivers of observed trends in freshet characteristics, whilst clarifying the effects of regulation versus climate at the sub-basin scale.
Management, skills and productivity

This paper studies how industries’ investment in organisational capital (OC) and workforce skills relate to productivity, building on OECD estimates of OC, output data from the OECD Structural Analysis (STAN) database, and both cognitive and task-based skill indicators from the OECD Programme for the International Assessment of Adult Competencies (PIAAC). The paper finds that at the industry level, workers’ numeracy and endowment of skills related to science, technology, engineering and mathematics (STEM) correlate positively with productivity, and that the positive correlation of STEM skills with productivity is generally larger for OC workers. The paper also finds evidence that skills dispersion harms industry performance. A gap between the ICT skills of OC and non-OC workers seems to trigger a “lost in translation” type of mechanism, whereby communication and information flows become less fluid and impinge upon the economic performance of sectors, correlating negatively with productivity.
Evaluation and analysis of comprehensive performance of a brake pedal based on an improved analytic hierarchy process

Due to the considerable increase in car parc and the remarkable improvement in vehicle properties and cost performance, higher requirements are being placed on driving comfort. With regard to the braking system, the enhancement of the brake pedal feel is one of the ways to improve the comfort, and affecting factors include spatial layout, dynamic characteristics, and the characteristics matching with the vehicle motion, so a comprehensive evaluation of a brake pedal is highly essential. This paper, by taking passenger cars in China as the research object, established a comprehensive evaluation system for pedals based on the improved analytic hierarchy process. The index weight in each index level was determined through the G1 method, and an experimental design was conducted on individual indices on the lowest level. The relationship parameters between the indices were measured through the test and comprehensive scores of each automobile pedal were obtained in combination with the evaluation method designed. Based on comprehensive scores, a typical analysis on 16 test vehicles was carried out, followed by a comparison of different index levels. Through result analysis, the direction for pedal improvement was proposed, and the feasibility of the method with the case is verified, which provides a scientific basis and a reference for vehicle pedal development, the calibration in position layout, and the pedal design.
How do Physicians Manage Functional Neurological Symptom Disorder and Somatic Symptom Disorder in the Emergency Department? A Vignette Study

Introduction: We aimed to explore how physicians from different specialties approach the management of functional neurological symptom (conversion) and somatic symptom disorders in the emergency department compared with pulmonary embolism and how physicians’ professional and personal characteristics influence their diagnostic preferences. Methods: Using a vignette methodology, and cross-sectional design, three emergency department case vignettes of possible functional neurological symptom, somatic symptom disorder, and pulmonary embolism were presented to physicians from internal medicine, emergency medicine, and psychiatry. A structured survey including questions on diagnosis and management of these cases, and physicians’ professional and personal characteristics (childhood trauma, attachment style) was conducted. Results: Physicians from internal medicine and emergency medicine tended to consider functional neurological symptom disorder as ‘malingering’ while psychiatrists tended to diagnose the pulmonary embolism case as a psychiatric condition. Emergency medicine physicians preferred to manage functional neurological symptom disorder themselves, while other doctors endorsed recommending a psychiatric consultation. In the univariable model, being a physician from psychiatry, emergency medicine, or internal medicine; being a specialist, history of childhood sexual abuse, dismissive, and fearful attachment styles of doctors were significant predictors of diagnosing functional neurological symptom disorder as malingering. Being a psychiatrist stayed as the only significant predictor in the multivariable model. Conclusion: Objectively-aberrant functional neurological symptoms and subjective somatic symptoms may be creating different reactions in physicians. In the emergency department, physicians’ diagnostic and treatment preferences of conversion disorder may be influenced by lack of training in conversion disorder, rather than their personal characteristics.
Direct Adaptive Fuzzy Control Scheme With Guaranteed Tracking Performances for Uncertain Canonical Nonlinear Systems

In our recent work, we propose an indirect adaptive fuzzy control scheme for uncertain unparametrizable nonlinear systems, which ensures that the number of adaptive laws does not increase with the number of fuzzy rules, and the time derivative of the chosen Lyapunov function is negative semidefinite. However, the scheme involves a class of high-order smooth functions and their time derivatives, which can make the controller structure become sophisticated especially when the relative degree of system is quite large. To overcome this problem, in the article, we propose a new direct adaptive fuzzy control scheme based on a class of reduced-order smooth functions. With the scheme, no partial-derivative term is involved in controller and virtual controllers, only one adaptive law is used regardless of the increase of fuzzy rules, and also the time derivative of Lyapunov function can be ensured negative semidefinite. It is proved that all closed-loop signals are bounded, and the output tracking error converges to a prescribed interval asymptotically. The transient tracking performance and robustness of the proposed control scheme are also considered. The effectiveness of the obtained results is illustrated by two practical control systems.
Recursive Filtering for Time-Varying Discrete Sequential Systems Subject to Deception Attacks: Weighted Try-Once-Discard Protocol

In this article, recursive filtering is investigated for time-varying discrete sequential systems (DSSs) under weighted try-once-discard (WTOD) protocols, which are employed to govern the access authorization of a shared network in order to remit the communication burden. A transmission model, dependent on a Bernoulli distributed white sequence, is developed to describe the phenomenon of deception attacks. In light of the adopted protocol and the attack model, a recursive algorithm with the form of Riccati-like difference equations is developed to optimize the filtering performance in the mean square sense. Furthermore, by resorting to the mathematical induction, the convergence of the proposed recursive algorithm is discussed profoundly. Finally, a simulation example is presented to verify the availability of the designed recursive filter.
The role of innovation and human capital for the productivity of industries

This paper sheds light on the relationship between innovation, human capital endowment and upgrading, organisational capital (OC) and labour productivity. In addition to assessing correlations, it uses a Heckman selection model to address causal links and to account for the ways in which skills and investment in R&D affect the probability of innovating. The analysis finds that innovative output, the proportion of OC-related workers, investment in training (especially in informal training) and physical capital intensity are positively and significantly related to productivity. In most estimates ICT skills, cognitive skills and the presence of highly skilled workers in an industry also emerge as having a significant and positive relationship with productivity. ICT skills further appear to indirectly shape productivity, through a positive relationship with innovation.
Screw Dynamic Modeling and Novel Composite Error-Based Second-order Sliding Mode Dynamic Control for a Bilaterally Symmetrical Hybrid Robot

SUMMARY In this research, a dynamic model is first established based on screw theory and the principle of virtual work for a bilaterally symmetrical hybrid robot. By combining a novel composite error (NCE) with second-order nonsingular fast terminal sliding mode (SONFTSM) control method, a NCE-based SONFTSM dynamic control method is further presented to guarantee better trajectory tracking performance and synchronization performance simultaneously. The asymptotic convergence of proposed errors and the stability of the proposed control method have been proved theoretically. Finally, the simulation and experiment are implemented to validate the effectiveness of the proposed control method.