Miracle-dz/UniFAR-FTUs huggingface dataset:
Leveraging Structural Cues for Better Training and Deployment in Computer Vision

With the growing use of computer vision tools in wide-ranging applications, it becomes imperative to understand and resolve issues in computer vision models when they are used in production settings for various applications. In particular, it is essential to understand that the model can be wrong quite frequently during deployment. Developing a better understanding of the mistakes made by a model can help mitigate and handle them without catastrophic consequences. To investigate the severity of mistakes, we first explore this in a simple classification setting. Even in this setting, understanding the severity of mistakes of difficult to quantify, especially since manually defining pairwise costs does not scale well for large-scale classification datasets. Therefore most works have used class taxonomies/hierarchies, which allow pairwise costs to be defined using graph distances. There has been increasing interest in building deep hierarchy-aware classifiers, aiming to quantify and reduce the severity of mistakes and not just count the number of errors. However, most of these works require the hierarchy to be available during training and cannot adapt to new hierarchies or even small modifications to the existing hierarchy without having to re-train the model. We explore a different direction for hierarchy-aware classification – amending mistakes by making post-hoc corrections by resorting to the classical Conditional Risk Minimization(CRM). Surprisingly, we find that this method is a far more suitable alternative than the works on deep hierarchy-aware classification; CRM preserves the base model’s top-1 accuracy and brings the most likely predictions of the model closer to the ground truth and is able to provide reliable probability estimates, unlike hierarchy-aware classifiers. We firmly believe that this serves as a very strong and useful baseline for future exploration in this direction. We turn our attention to a crucial problem in many video processing pipelines: visual(single) object tracking. In particular, we explore the long-term tracking scenario where given a target in the first frame of the video; the goal is to track the object throughout a (long) video during which the object may undergo occlusion, vary in appearance, or go out-of-view. The temporal aspect of videos also makes it an ideal scenario to understand the accumulation of errors that would not be otherwise seen if every image is independent. We hypothesize that there are three crucial abilities that a tracker must possess to be effective in the long-term tracking scenario, namely Re-Detection, Recovery and, Reliability. The tracker must be able to re-detect the target when the target goes out of the scene, and returns must recover from failure and track an object contiguously to be of practical utility. We propose a set of novel and com-
Human Computation Recommender for Inter-Enterprise Data Sharing and ETL Processes

—Extraction, Transforming and Loading (ETL) is an important and complex process of data integration. In this paper, we review existing ontology-based ETL methods. We then analyze why ontologies could be a better approach to ETL compared to other approaches such as folksonomies. Following this, we describe inter-enterprise collaboration, a class of problem that requires improvements to the overall ETL process. We also describe and analyze three case studies to illustrate their importance to this research.
Ciclone Michael: gênese e transição extratropical

Os ciclones de escala sinotica sao responsaveis por grandes mudancas no tempo das regioes onde atuam. Nos ultimos anos tem aumentado o numero de sistemas tropicais severos. Por exemplo, o ciclone Michael, ocorrido em outubro de 2018, causou muitos danos nos Estados Unidos (U$25 bilhoes em prejuizo), inclusive 16 obitos. Diante desse contexto, o objetivo do presente estudo e a analise sinotica da genese e transicao extratropical do ciclone Michael. Michael teve genese no mar do Caribe, no dia 6 de outubro de 2018, associada a uma perturbacao ciclonica em baixos niveis da atmosfera. O sistema chegou a categoria 5 na escala de Saffir-Simpson no dia 10 de outubro; ja no dia 11 de outubro transicionou para ciclone extratropical e decaiu no dia 18 de outubro. A analise sinotica mostra que transicao extratropical ocorre a medida que o sistema interage com uma regiao de intenso gradiente horizontal de temperatura do ar. Cyclone Michael: genesis and extratropical transition A B S T R A C T Synoptic-scale cyclones are responsible for major changes in the weather in the regions where they act. In recent years the number of severe tropical systems has increased. For example, cyclone Michael, which occurred in October 2018, caused a lot of damage in the United States ($ 25 billion in damage) including 16 deaths. Given this context, the objective of the present study is the synoptic analysis of the genesis and extratropical transition of cyclone Michael. Michael had genesis in the Caribbean Sea on October 6, 2018, associated with a cyclonic disturbance in low levels of the atmosphere. The system reached category 5 on the Saffir-Simpson scale on 10 October; on the 11 October it transitioned to an extratropical cyclone and decayed on the 18 October. The synoptic analysis shows that extratropical transition occurs as the system interacts with a region of intense horizontal air temperature gradient. Keywords: cyclone, synoptic analysis, extratropical transition.
Adaptive hysteresis band current control of grid connected PV inverter

In this paper, adaptive hysteresis band current controller is implemented to control the current injected into the grid. Initially it was implemented by B.K Bose for control of the machine drive. Now it is implemented for the grid connected PV inverter, to control the current injected into Grid. It is well suitable for the distribution generation. The adaptive hysteresis band controller changes the bandwidth based on the modulating frequency, supply voltage, input DC voltage and slope of the reference current. Consequently, the controller generates pulses to the inverter. It is advantageous over the conventional hysteresis controller, as the switching frequency is maintained almost constant. Thereby quality of grid current is also improved. It is verified in time domain analysis of simulation using MATLAB.
A novel estimator for the equation of state of the IGM by Ly α forest tomography

We present a novel procedure to estimate the Equation of State of the intergalactic medium in the quasi-linear regime of structure formation based on Ly$\alpha$ forest tomography and apply it to 31 high quality quasar spectra from the UVES_SQUAD survey at redshift $z=2.5$. Our estimation is based on a full tomographic inversion of the line of sight. We invert the data with two different inversion algorithms, the iterative Gauss-Newton method and the regularized probability conservation approach, which depend on different priors and compare the inversion results in flux space and in density space. In this way our method combines fitting of absorption profiles in flux space with an analysis of the recovered density distributions featuring prior knowledge of the matter distribution. Our estimates are more precise than existing estimates, in particular on small redshift bins. In particular, we model the temperature-density relation with a power law and observe for the temperature at mean density $T_0 = 13000^{+1300}_{-1200}\,\mathrm{K}$ and for the slope of the power-law (polytropic index) $\gamma = 1.44 \pm 0.09$ for the power-law parameters describing the temperature-density relation. Moreover, we measure an photoionization rate $\Gamma_{-12} = 1.12 \pm 0.17$. An implementation of the inversion techniques used will be made publicly available.
The trend malware source of IoT network

Malware may disrupt the Internet of Thing (IoT) system/network when it resides in the network, or even harm the network operation. Therefore, malware detection in the IoT system/network becomes an important issue. Research works related to the development of IoT malware detection have been carried out with various methods and algorithms to increase detection accuracy. The majority of papers on malware literature studies discuss mobile networks, and very few consider malware on IoT networks. This paper attempts to identify problems and issues in IoT malware detection presents an analysis of each step in the malware detection as well as provides alternative taxonomy of literature related to IoT malware detection. The focuses of the discussions include malware repository dataset, feature extraction methods, the detection method itself, and the output of each conducted research. Furthermore, a comparison of malware classification approaches accuracy used by researchers in detecting malware in IoT is presented.
The future of software engineering: Visions of 2025 and beyond

In the current technological scenario of the industry and businesses, there has been increasing need of software within systems and also an increasing demand being put onto software-intensive systems. This in effect will lead to a significant evolution of software engineering processes over the next twenty years. This is due to the fact of emerging technological advancements like Industry 4.0 and Internet of Things in the IT field, among other new developments. This paper addresses and tries to analyses the key research challenges being faced by the software engineering field and articulates information that is derived from the key research specializations within software engineering. The paper analyses the past and current trends in software engineering. The future of software engineering is also looked with respect to Industry 4.0 which including emerging technological platforms like Internet of Things. The societal impact aspect of future trends in software engineering is also addressed in this paper.
Novel Heuristic for Low-Batch Manufacturing Process Scheduling Optimisation with Reference to Process Engineering

Scheduling is an important element that has a major impact on the efficiency of all manufacturing processes. It plays an important role in optimising the manufacturing times and costs resulting in energy efficient processes. It has been estimated that more than 75% of manufacturing processes occur in small batches. In such environments, processes must be able to perform a variety of operations on a mix of different batches. Batch-job scheduling optimisation is the response to such low batch manufacturing problems. The optimisation of batch-job process scheduling problem is still a challenge to researchers and is far from being completely solved due to its combinatorial nature. In this paper, a novel hybrid heuristic (HybH) solution approach for batch-job scheduling problem is presented with the objective of optimising the overall Makespan (Cmax). The proposed HybH is the combination of Index Based Heuristic (IBH) and the Finished Batch-Job (FBJ) process schedule. The heuristic assigns the first operation to a batch-job using IBH and the remaining operations on the basis FBJ process schedule. The FBJ process schedule gives priority to the batch-job with early finished operations, without violating the constraints of process order. The proposed HybH is explained with the help of a detailed example. Several benchmark problems are solved from the literature to check the validity and effectiveness of the proposed heuristic. The presented HybH has achieved batch-job process schedules which have outperformed the traditional heuristics. The results are encouraging and show that the proposed heuristic is a valid methodology for batch process scheduling optimisation.
Calculation of absolute molecular entropies and heat capacities made simple

We propose a fully-automated composite scheme for the accurate and numerically stable calculation of molecular entropies by efficiently combining density-functional theory (DFT), semi-empirical methods (SQM), and force-field (FF) approximations. The scheme is systematically expandable and can be integrated seamlessly with continuum-solvation models. Anharmonic effects are included through the modified rigid-rotor-harmonic-oscillator (msRRHO) approximation and the Gibbs–Shannon formula for extensive conformer ensembles (CEs), which are generated by a metadynamics search algorithm and are extrapolated to completeness. For the first time, variations of the ro-vibrational entropy over the CE are consistently accounted-for through a Boltzmann-population average. Extensive tests of the protocol with the two standard DFT approaches B97-3c and B3LYP-D3 reveal an unprecedented accuracy with mean deviations <1 cal mol−1 K−1 (about <1–2%) for the total gas phase molecular entropy of medium-sized molecules. Even for the hardship case of extremely flexible linear alkanes (C14H30–C16H34), errors are only about 3 cal mol−1 K−1. Comprehensive tests indicate a relatively strong variation of the conformational entropy on the underlying level of theory for typical drug molecules, inferring the complex potential energy surfaces as the main source of error. Furthermore, we show some application examples for the calculation of free energy differences in typical chemical reactions.
Unequal clustering in wireless sensor network: a review

In recent time, the applications' diversity of wireless sensor network (WSN) attracts many researchers. WSN comprises of many sensor nodes with limited battery power. Therefore, energy consumption should be controlled to the optimum. Clustering is an efficient solution for energy management in WSN, but clustering does not consider the sink node location. It will cause the energy hole problem in multi-hop routing. Energy hole problem was solved by unequal clustering. A review of various unequal clustering mechanisms is presented in this paper. The comparison between the various mechanisms was based on cluster head election process, cluster properties, simulation parameters and energy efficiency to highlight a more efficient and scalable unequal clustering algorithm for WSN.
Microneedle Arrays for Sampling and Sensing Skin Interstitial Fluid

Dermal interstitial fluid (ISF) is a novel source of biomarkers that can be considered as an alternative to blood sampling for disease diagnosis and treatment. Nevertheless, in vivo extraction and analysis of ISF are challenging. On the other hand, microneedle (MN) technology can address most of the challenges associated with dermal ISF extraction and is well suited for long-term, continuous ISF monitoring as well as in situ detection. In this review, we first briefly summarise the different dermal ISF collection methods and compare them with MN methods. Next, we elaborate on the design considerations and biocompatibility of MNs. Subsequently, the fabrication technologies of various MNs used for dermal ISF extraction, including solid MNs, hollow MNs, porous MNs, and hydrogel MNs, are thoroughly explained. In addition, different sensing mechanisms of ISF detection are discussed in detail. Subsequently, we identify the challenges and propose the possible solutions associated with ISF extraction. A detailed investigation is provided for the transport and sampling mechanism of ISF in vivo. Also, the current in vitro skin model integrated with the MN arrays is discussed. Finally, future directions to develop a point-of-care (POC) device to sample ISF are proposed.
Classification of rice plant nitrogen nutrient status using k-nearest neighbors (k-NN) with light intensity data

Crop management including the efficient use of nitrogen (N) fertilizer is important to ensure crop productivity. Human error in judging the leaf greenness when using the leaf color chart (LCC) to estimate the rice plant N nutrient status has encouraged numerous researchers to implement a machine-learning algorithm but experienced some issues in calibration and lighting. The datasets are created at 6.00-7.00AM (consistent lighting) and including light intensity, so each dataset contains RGB value and light intensity as inputs, and LCC value as a target. A system consists of a smartphone with an application that prevents user from taking an image if the light intensity is not in 2000-3500 lux, and a computer for preprocessing and classification purposes were developed. The preprocessing included cropping, splitting the rice leaf images, and calculating the average RGB values. A k-NN classifier is implemented and by using a cross-validation method is found k=5 gives the best accuracy of 97,22%. The in-site test of the system also works with an accuracy of 96.40%.
SAR distribution of non-invasive hyperthermia with microstrip applicators on different breast cancer stages

This paper presents the microstrip antenna with different applied frequencies used as a non-invasive hyperthermia applicator. This non-invasive hyperthermia applicator is introduced to clarify the sufficient heat distribution on the treated tissue for different breast cancer stages. 57 mammogram breast cancer images from early-stage to stage- 3 are analyzed to obtain the required penetration depth and focus position distance. Then, the simulation-based experiment is carried out to observe the heating distribution on different stages of cancer with two different operating frequencies; 915MHz and 2450MHz. Also included in this paper is the prediction on the period for hyperthermia treatment planning execution. Based on the results, various penetration depths are obtained when different operating frequencies are applied. 915MHz antenna showed better results when compared to 2450MHz, where microstrip applicator with 915MHz is able to heat cancer at stage-1, stage-2, and stage-3 with good penetration depth and focus position distance, while 2450MHz only performed well in early-stage cancer. Meanwhile, different stages require various periods of time. From the results, the shortest period for hyperthermia execution simulated in the early-stage and then followed by stage-3, stage-2, and the longest period is in stage-1.
The impact threshold of the aerosol radiative forcing on the boundary layer structure in the pollution region

Abstract. Recently, there has been increasing interest in the relation between particulate matter (PM) pollution and atmospheric-boundary-layer (ABL) structure. This study aimed to qualitatively assess the interaction between PM and ABL structure in essence and further quantitatively estimate aerosol radiative forcing (ARF) effects on the ABL structure. Multi-period comparative analysis indicated that the key to determining whether haze outbreak or dissipation occurs is whether the ABL structure satisfies the relevant conditions. However, the ABL structure change was in turn highly related to the PM level and ARF. | SFC − ATM | (SFC and ATM are the ARFs at the surface and interior of the atmospheric column, respectively) is the absolute difference between ground and atmosphere layer ARFs, and the | SFC − ATM | change is linearly related to the PM concentrations. However, the influence of ARF on the boundary layer structure is nonlinear. With increasing | SFC − ATM | , the turbulence kinetic energy (TKE) level exponentially decreased, which was notable in the lower layers or ABL, but disappeared at high altitudes or above the ABL. Moreover, the ARF effects threshold on the ABL structure was determined for the first time, namely once | SFC − ATM | exceeded ∼55 W m −2 , the ABL structure tends to quickly stabilize and thereafter change little with increasing ARF. The threshold of the ARF effects on the boundary layer structure could provide useful information for relevant atmospheric-environment improvement measures and policies, such as formulating phased air pollution control objectives.
Generation and collection of data for normal and conflicting flows in software defined network flow table

In terms of network simplification and regulation, Software Defined Networking (SDN) is a new form of infrastructure that offers greater adaptability and flexibility. SDN, however, is an invention that is logically centralized. In addition, the optimization of the control plane and data plane in SDN has become an area deserving of more attention. The flow in OpenFlow has been one of the essential parameters in the SDN standards, in which every individual flow includes packet matching fields, flow priority, separate counters, instructions for packet forwarding, flow timeouts and a cookie. This research work is conducted in order to produce and collect flows from the OpenFlow switch in two scenarios; in normal flows and when conflict policy rules are enforced in the network. In this article, the throughput is required to review and evaluate the conflict impact on two protocols as a performance metric; the Transmission Control Protocol (TCP) and the User Datagram Protocol (UDP) that flows via a forwarded plane. During the simulation of the SDN OpenFlow network, the metrics are tested using MININET. The results demonstrate that the existence of SDN conflict rules allows TCP and UDP to have a significant average change in bandwidth that eventually affects the network and operations performance.
Spatial and temporal variation of fecal indicator organisms in two creeks in Beltsville, Maryland

Evaluation of microbial water quality is commonly based on monitoring populations of fecal indicator organisms (FIO) such as E. coli (EC) and enterococci. The occurrence of elevated FIO concentrations in surface waters after storm events is well documented and has been attributed to runoff and sediment resuspension. The reasons for FIO concentration variation under baseflow conditions are less clear. The objective of this study was to quantify the variability of EC and enterococci in two small streams running through agricultural land use areas. FIO concentrations were measured at upstream and downstream locations under baseflow conditions. Concentrations were not significantly different along cross-sections of the streams. Diurnal concentration trends were observed at each of the sampling locations. Significant differences in concentrations between upstream and downstream locations were noted for both creeks during baseflow periods when no runoff or sediment resuspension occurred. A hypothetical explanation is that indicator organisms are released from sediments during baseflow conditions due to the effect of groundwater influx into streams or due to the motility of indicator organisms. If confirmed, this hypothesis may affect our understanding of the role of sediments in the microbial quality of surface waters.