Miracle-dz/UniFAR-FTUs huggingface dataset:
Predictive Modeling with Delayed Information: a Case Study in E-commerce Transaction Fraud Control

In Business Intelligence, accurate predictive modeling is the key for providing adaptive decisions. We studied predictive modeling problems in this research which was motivated by real-world cases that Microsoft data scientists encountered while dealing with e-commerce transaction fraud control decisions using transaction streaming data in an uncertain probabilistic decision environment. The values of most online transactions related features can return instantly, while the true fraud labels only return after a stochastic delay. Using partially mature data directly for predictive modeling in an uncertain probabilistic decision environment would lead to significant inaccuracy on risk decision-making. To improve accurate estimation of the probabilistic prediction environment, which leads to more accurate predictive modeling, two frameworks, Current Environment Inference (CEI) and Future Environment Inference (FEI), are proposed. These frameworks generated decision environment related features using long-term fully mature and short-term partially mature data, and the values of those features were estimated using varies of learning methods, including linear regression, random forest, gradient boosted tree, artificial neural network, and recurrent neural network. Performance tests were conducted using some e-commerce transaction data from Microsoft. Testing results suggested that proposed frameworks significantly improved the accuracy of decision environment estimation.
Thermoelectric Energy Harvesting Interface Circuit With Capacitive Bootstrapping Technique for Energy-Efficient IoT Devices

This paper presents a low-input-voltage (100 mV), low-output-voltage (500–600 mV) thermoelectric energy harvesting interface circuit for near-threshold energy-efficient Internet-of-Things (IoT) devices. The capacitive bootstrapping technique is used to generate a positive and negative bias pair for alleviating the significant conduction losses of power MOSFETs in a near-threshold operation. Internal bias voltages are automatically boosted to different levels as per the loading conditions to extend the output power range. The deployment of constant on-time digital pulse skip modulation with digital zero current detection (ZCD) achieves an ultralight load operation, and precluding a reverse current. The digital ZCD is capable of dynamically adjusting the off-time (<inline-formula> <tex-math notation="LaTeX">${T}_{\mathrm {OFF}}$ </tex-math></inline-formula>) of the power transistors, which vary according to the input and output voltage levels. The proposed step-up dc-dc power converter implemented using a 180 nm CMOS technology demonstrates a maximum conversion efficiency of 76.4% over a <inline-formula> <tex-math notation="LaTeX">$1~{\mu }\text{W}$ </tex-math></inline-formula>–<inline-formula> <tex-math notation="LaTeX">$500~{\mu }\text{W}$ </tex-math></inline-formula> load range, significantly evaluating the feasibility of the near-threshold interface circuit architecture for energy-efficient IoT devices.
EmoBrain: Playing with Emotions in the Target

Sensing and understanding human emotional behaviour seems to be essential to keep engaging interactions sustained over longer periods. The general purpose of this work is to implement a platform able to recognize and employ human emotions in an interactive game: The EmoBrain Interface (EI). EI interaction is a cycle of stimulus and feedbacks where the user receives a visual input and completes tasks, just controlling his emotional state by activating self-training strategies. EI has been used in the context of a serious game: Quiet Bowman (QB). QB allows the users: (i) to experience emotional behaviours and (ii) to explore the game dynamics related to emotions in order to manage their own emotional state. The outcomes seem to be encouraging. Users appreciated the game: they felt involved and committed to achieve the required goals. This encourages us to make new experiments to improve the accuracy of our classifiers and therefore the impact of the serious game on the autogenous training of the user. Author
Characterisation of Monovarietal Olive Oils Obtained from Croatian cvs. Drobnica and Buza during the Ripening Period

The aim of this study was the monitoring of the chemical composition of olive oil at different ripening stages to determine the appropriate harvesting time during any given crop season in the northern Adriatic region. For this purpose, from September to November, two Croatian olive cultivars (Drobnica and Buza) were taken from two different olive orchards and for the respective olive oils, prepared on a laboratory scale, the major saponifiable, unsaponifiable and phenolic compounds were determined. Based on the chemical analyses performed, the optimal harvesting time has been set in October for both cultivars. Buza had a higher oleic acid, but lower total sterols, squalene and total alkanols. Compared to the local cultivars, the studied cultivars had a high total phenolic content and antioxidant activity. The antioxidant activity and concentrations of total phenols correlated with α-tocopherol in oil samples taken during the ripening progress. Finally, trace minerals detected in Buza and Drobnica oil differed, which can be an indicator of oxidative stability and authenticity of oils.
The Length Distribution and Multiple Specificity of Naturally Presented HLA-I Ligands

HLA-I molecules bind short peptides and present them for recognition by CD8+ T cells. The length of HLA-I ligands typically ranges from 8 to 12 aa, but variability is observed across different HLA-I alleles. In this study we collected recent in-depth HLA peptidomics data, including 12 newly generated HLA peptidomes (31,896 unique peptides) from human meningioma samples, to analyze the peptide length distribution and multiple specificity across 84 different HLA-I alleles. We observed a clear clustering of HLA-I alleles with distinct peptide length distributions, which enabled us to study the structural basis of peptide length distributions and predict peptide length distributions from HLA-I sequences. We further identified multiple specificity in several HLA-I molecules and validated these observations with binding assays. Explicitly modeling peptide length distribution and multiple specificity improved predictions of naturally presented HLA-I ligands, as demonstrated in an independent benchmarking based on the new human meningioma samples.
Neuroprotective Effects on the Morphology of Somatic Motoneurons Following the Death of Neighboring Motoneurons: A Role for Microglia?

Partial depletion of spinal motoneuron populations induces dendritic atrophy in neighboring motoneurons, and treatment with testosterone protects motoneurons from induced dendritic atrophy. We explored a potential mechanism for this induced atrophy and protection by testosterone, examining the microglial response to partial depletion of motoneurons. Motoneurons innervating the vastus medialis muscles of adult male rats were killed by intramuscular injection of cholera toxin‐conjugated saporin; some saporin‐injected rats were treated with testosterone. Microglia were later visualized via immunohistochemical staining, classified as monitoring or activated, and counted stereologically. Partial motoneuron depletion increased the number of activated microglia in the quadriceps motor pool, and this increase was attenuated with testosterone treatment. The attenuation in microglial response could reflect an effect of testosterone on suppressing microglia activation, potentially sparing motoneuron dendrites. Alternatively, testosterone could be neuroprotective, sparing motoneuron dendrites, secondarily resulting in reduced microglial activation. To discriminate between these hypotheses, following partial motoneuron depletion, rats were treated with minocycline to inhibit microglial activation. Motoneurons innervating the ipsilateral vastus lateralis muscle were later labeled with cholera toxin‐conjugated horseradish peroxidase, and dendritic arbors were reconstructed. Reduction of microglial activation by minocycline did not prevent induced dendritic atrophy following partial motoneuron depletion. Further, reduction of microglial activation by minocycline treatment resulted in dendritic atrophy in intact animals. Together, these findings indicate that the neuroprotective effect of testosterone on dendrites following motoneuron death is not due to inhibiting microglial activation, and that microglial activity contributes to the normal maintenance of dendritic arbors.
Evolution of Enterprise Architecture for Digital Transformation

The digital transformation of our life changes the way we work, learn, communicate, and collaborate. Enterprises are presently transforming their strategy, culture, processes, and their information systems to become digital. The digital transformation deeply disrupts existing enterprises and economies. Digitization fosters the development of IT systems with many rather small and distributed structures, like Internet of Things, Microservices and mobile services. Since years a lot of new business opportunities appear using the potential of services computing, Internet of Things, mobile systems, big data with analytics, cloud computing, collaboration networks, and decision support. Biological metaphors of living and adaptable ecosystems provide the logical foundation for self-optimizing and resilient run-time environments for intelligent business services and adaptable distributed information systems with service-oriented enterprise architectures. This has a strong impact for architecting digital services and products following both a value-oriented and a service perspective. The change from a closed-world modeling world to a more flexible open-world composition and evolution of enterprise architectures defines the moving context for adaptable and high distributed systems, which are essential to enable the digital transformation. The present research paper investigates the evolution of Enterprise Architecture considering new defined value-oriented mappings between digital strategies, digital business models and an improved digital enterprise architecture.
Secret Information Embedding with Two JPEG Images for those Same Scene.

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Topic Modeling of Conflict Ad Hoc Discussions in Social Networks

In this paper authors carry out the research of three different topic models in the context of analyzing different large scale twitter ad hoc discussions, obtained from social network on different social and political events. An experiment is being conducted to test the effectiveness of models in analyzing three known discussions: the riots in Biryulyovo (Russia), Ferguson unrest (USA), Charlie Hebdo shooting (France). The results of the experiment show that the BTM topic model in terms of both Umass and Npmi, yielded the best results on all discussions in comparison to the baseline LDA model and another model for short unbalanced texts - WNTM.
Solving Inverse Kinematics Model for 7-DoF Robot Arms Based on Space Vector

The 7-DoF Manipulator has a high degree of flexibility and can perform many complex tasks for humans, therefore widely used in many fields. This paper proposes a new method to solve the inverse kinematics model for 7-DoF manipulators. Specifically, it focuses on how to derive equations for the feasible space of the endpoint of each joint and correspondingly the feasible space of each arm-angle when the endpoint of the robot arm is given, which lays the foundation for the robotic arm to complete obstacle avoidance and optimal path planning tasks. First, the influence of the first three joints and the last three joints on the end position of the robotic arm is decoupled. Based on this decoupling, the relationship between the last three joint angles and the end position of the robotic arm is solved through the space vector. Furthermore, the relationship between the end position of the robotic arm and the first three joint angles is obtained through coordinate rotation. Finally, this paper validates the results by simulations.
Unintentional Genomic Changes Endow Cupriavidus metallidurans with an Augmented Heavy-Metal Resistance

For the past three decades, Cupriavidus metallidurans has been one of the major model organisms for bacterial tolerance to heavy metals. Its type strain CH34 contains at least 24 gene clusters distributed over four replicons, allowing for intricate and multilayered metal responses. To gain organic mercury resistance in CH34, broad-spectrum mer genes were introduced in a previous work via conjugation of the IncP-1β plasmid pTP6. However, we recently noted that this CH34-derived strain, MSR33, unexpectedly showed an increased resistance to other metals (i.e., Co2+, Ni2+, and Cd2+). To thoroughly investigate this phenomenon, we resequenced the entire genome of MSR33 and compared its DNA sequence and basal gene expression profile to those of its parental strain CH34. Genome comparison identified 11 insertions or deletions (INDELs) and nine single nucleotide polymorphisms (SNPs), whereas transcriptomic analysis displayed 107 differentially expressed genes. Sequence data implicated the transposition of IS1088 in higher Co2+ and Ni2+ resistances and altered gene expression, although the precise mechanisms of the augmented Cd2+ resistance in MSR33 remains elusive. Our work indicates that conjugation procedures involving large complex genomes and extensive mobilomes may pose a considerable risk toward the introduction of unwanted, undocumented genetic changes. Special efforts are needed for the applied use and further development of small nonconjugative broad-host plasmid vectors, ideally involving CRISPR-related and advanced biosynthetic technologies.
MR Brain Image Enhancement via Learning Ensemble

Magnetic resonance (MR) brain images often have low contrast due to different selections of pulse sequence parameters. Histogram equalization method is one of the most popular image enhancement techniques for improving quality of digital images. However, a single histogram equalizer is not able to detect details of contrast existing in the whole image and enhanced results are often distorted. Inspired by machine learning ensemble, a strategy using multiple histogram equalizers on local windows has been applied on enhancing natural images. In this paper, this strategy is investigated further again and applied on MR brain images for contrast enhancement. Each single histogram equalizer on local patch of MR brain image is considered as a single classifier or predictor in machine learning ensemble. A global normalization is used for enhancing the whole image from multiple histogram equalizers. The effect of noise amplification of accelerated MR data acquisition on image enhancement is also studied. The experimental results demonstrate that the proposed method not only enhances contrast among brain tissues (like white matter, grey matter, and cerebrospinal fluid) of MR brain images, but also outperforms other image enhancement methods for improving image quality.
The Closed-Form Solution of Frequency Shift for an HF RFID Coil Antenna in Metallic Environments

The performance of a high frequency radio frequency identification (RFID) coil antenna is strongly dependent on its operating environments. The presence of the metallic environments can shift up the resonant frequency of the antenna and change its field intensity. This paper presents a novel equivalent model based on image theory to study quantitatively the proximity effects of metallic environments on the antenna. From the proposed model an equivalent circuit has been extracted, and it is used to derive the frequency shift formulas of the antenna based on the theory of magnetic resonant coupling. Then the closed-form solution of operating frequency for the antenna in metallic environments can be obtained in terms of the geometric parameters of coil antenna as well as the distance between the coil antenna and the metal plate using the proposed formulas. To confirm the validity of the proposed model, simulations have been carried out and compared with the measurement results. The results show that the proposed model can predict the operating frequency of the antenna in metallic environments and the distribution of magnetic field accurately and rapidly. The closed-form solution of frequency shift derived from this paper can be helpful to engineers to enhance the RFID antennas performance by alleviating the effects of metallic environments, which is pivotal for the Internet of Things technology.
MEETS: Maximal Energy Efficient Task Scheduling in Homogeneous Fog Networks

A homogeneous fog network is defined as a group of peer nodes with sharable computing and storage resources, as well as spare spectrum for node-to-node/device-to-device communications and task scheduling. It promotes more intelligent applications and services in different Internet of Things (IoT) scenarios, thanks to effective collaborations among neighboring fog nodes via cognitive spectrum access techniques. In this paper, a comprehensive analytical model that considers circuit, computation, offloading energy consumptions is developed for accurately evaluating the overall energy efficiency (EE) in homogeneous fog networks. With this model, the tradeoff relationship between performance gains and energy costs in collaborative task offloading is investigated, thus enabling us to formulate the EE optimization problem for future intelligent IoT applications with practical constraints in available computing resources at helper nodes and unused spectrum in neighboring environments. Based on rigorous mathematical analysis, a maximal energy-efficient task scheduling (MEETS) algorithm is proposed to derive the optimal scheduling decision for a task node and multiple neighboring helper nodes under feasible modulation schemes and time allocations. Extensive simulation results demonstrate the tradeoff relationship between EE and task scheduling performance in homogeneous fog networks. Compared with traditional task scheduling strategies, the proposed MEETS algorithm can achieve much better EE performance under different network parameters and service conditions.
De-pseudonymization of Smart Metering Data: Analysis and Countermeasures

Fine-grained metering data threatens users’ privacy, as it typically reveals the users’ consumption patterns and thereby their behaviour. To address this problem, the use of pseudonyms when sending such fine-grained data has been proposed in the literature. In this paper, we demonstrate experimentally that an adversary who has access to pseudonymized fine-grained data and identifiable billing data can fully de-pseudonymize all users using a simple matching algorithm. Our experiments use realworld metering data collected from ca. 6500 smart meters. As pseudonymization alone is not sufficient to provide privacy, we propose three simple yet effective countermeasures against depseudonymization: deliberately not reporting some of the finegrained metering values, rounding these values before reporting them and regularly changing the pseudonyms. We experimentally demonstrate that our countermeasures considerably improve users’ privacy protection without significantly lowering the usefulness of the data. They also do not affect the billing process.
18F‐Labeled Derivatives of Irbesartan for Angiotensin II Receptor PET Imaging

The renin angiotensin aldosterone system (RAAS) is a hormonal cascade involved in the regulation of blood pressure and electrolyte balance, and represents a common target for the treatment of various diseases including hypertension, heart failure, and diabetes. Herein we present a novel 18F‐labeled derivative of the drug irbesartan, one of the most prescribed angiotensin II type 1 receptor (AT1R) antagonists, for in vivo positron emission tomography (PET). This allows the in vivo measurement of AT1R expression, and thus the evaluation of functional changes in its expression under pathophysiological conditions. We followed various synthetic approaches optimized for the introduction of fluorine into different positions of the aliphatic side chain of irbesartan. Radioligand binding studies revealed that fluorine atoms at specified positions (α‐position (IC50=6.6 nm) and δ‐position (IC50=8.5 nm) of the aliphatic side chain) do not alter the binding properties of irbesartan (IC50=1.6 nm). After successful radiolabeling with fluorine‐18 in a radiochemical yield of 11 %, we observed high renal uptake in healthy rats and pigs, which could be decreased by pretreatment with the parent compound irbesartan.
Escaping the Trap: Losing the Northern Cities Shift in Real Time

Abstract We examine the loss of the Northern Cities Shift raising of trap in Ogdensburg, a small city in rural northern New York. Although data from 2008 showed robust trap-raising among young people in Ogdensburg, in data collected in 2016 no speakers clear the 700-Hz threshold for NCS participation in F1 of trap—a seemingly very rapid real-time change. We find apparent-time change in style-shifting: although older people raise trap more in wordlist reading than in spontaneous speech, younger people do the opposite. We infer that increasing negative evaluation of the feature led Ogdensburg speakers to collectively abandon raising trap between 2008 and 2016. This indicates a role for communal change in the transition of a dialect feature from an indicator to a marker.
Vision-based UAVs Aerial Image Localization: A Survey

Unmanned aerial vehicles (UAVs) have been increasingly used in earth observation, public safety, military and civilian applications due to its portability, high mobility and flexibility. In some GPS-denied environments, accurate drone position cannot be obtained due to occlusion, multi-path interference and other factors. While understanding and localization the content of the images is vital for earth observation, map revision, multi-source image fusion, disaster relief, smart city and other applications. The progress of computer vision and convolutional neural networks(CNNs) in image processing provide a promising solution to locate UAVs aerial image and mapping to the large-scale reference image. Firstly, key localization techniques based on image retrieval-----image description, image matching and position mapping are summarized considering the characteristics of UAVs aerial images. And then, image localization based on extracting deep semantic features and image localization based on classification method by subdividing areas are recommended. Throughout this paper, we will have an insight into the prospect of the UAVs image localization and the challenges to be faced.