Miracle-dz/UniFAR-FTUs huggingface dataset:
A High Frequency Vibration Compensation Approach in Terahertz SAR Based on Wavelet Multi-Resolution Analysis

The use of terahertz wave in SAR imaging can solve the difficulties of frame rate and detection of slow moving targets in conventional SAR imaging. The most important difference between terahertz SAR (THz-SAR) and conventional SAR is the treatment on motion compensation. For reason that the wavelength of terahertz SAR is much shorter than that of conventional microwave SAR, the tiny vibration of SAR platform will blur SAR images, especially high frequency components. The high frequency vibration will result in paired echoes in SAR imaging, which can't be focused with traditional SAR imaging algorithms. Thus the vibration parameters can't be estimated precisely enough to construct the reference function to compensate the sinusoidal modulation phase. So we first get focused paired echoes in terahertz SAR imaging through Doppler keystone transform (DKT), then we propose a frequency estimation method based on wavelet multi-resolution analysis, along with parametric space projection method, to complete the high frequency vibration estimation of terahertz SAR. At last, the numerical tests using the point target echoes validate the proposed method.
High-Performance and Cost-Effective ZCS Matrix Resonant Inverter for Total Active Surface Induction Heating Appliances

Flexible cooking surfaces represent the most innovative and high-performance induction heating appliances nowadays. This paper presents a multiple-output resonant inverter for multicoil systems featuring high efficiency and flexible output power control for modern induction heating appliances. By adopting a matrix structure, the number of controlled devices can be significantly reduced while high control versatility is ensured. The proposed converter is first analyzed and, in order to prove the feasibility of the proposal, a multiple-output prototype is designed and implemented. The experimental results prove the correct converter operation and output power control with multiple induction heating loads, validating the proposed approach.
Distributed Optimization over Lossy Networks via Relaxed Peaceman-Rachford Splitting: a Robust ADMM Approach

In this work we address the problem of distributed optimization of the sum of convex cost functions in the context of multi-agent systems over lossy communication networks. Building upon operator theory, first, we derive an ADMM- like algorithm, referred to as relaxed ADMM (R-ADMM) via a generalized Peaceman-Rachford Splitting operator on the Lagrange dual formulation of the original optimization problem. This algorithm depends on two parameters, namely the averaging coefficient $\alpha$ and the augmented Lagrangian coefficient $\rho$ and we show that by setting $\alpha =$ 1/2 we recover the standard ADMM algorithm as a special case. Moreover, first, we reformulate our R-ADMM algorithm into an implementation that presents reduced complexity in terms of memory, communication and computational requirements. Second, we propose a further reformulation which let us provide the first ADMM-like algorithm with guaranteed convergence properties even in the presence of lossy communication. Finally, this work is complemented with a set of compelling numerical simulations of the proposed algorithms over random geometric graphs subject to i.i. d. random packet losses.
Reducing the False Alarm Rate for Face Morph Detection by a Morph Pipeline Footprint Detector

In this paper, we introduce a novel multi-level process to reduce the false alarm rate (FAR) of existing state-of-the-art face morph detectors, designed to counter the threat that face morphing attacks represent for face image based authentification scenarios. Therefore, we design a novel morph pipeline footprint detector and a novel verification engine to validate the classification results of these existing detectors. The detectors are based on Benford features derived from JPEG DCT coefficients (in the face region of the image and background) and local derivative pattern features. We evaluate the morph pipeline footprint detector with more than 30,000 images and our morph verification engine with false classified authentic images of state-of-the-art approaches. The evaluation shows that our approach is able to reduce the false alarms by 83.67 %.
Comprehensive Privacy Analysis of Deep Learning: Stand-alone and Federated Learning under Passive and Active White-box Inference Attacks

Deep neural networks are susceptible to various inference attacks as they remember information about their training data. We perform a comprehensive analysis of white-box privacy inference attacks on deep learning models. We measure the privacy leakage by leveraging the final model parameters as well as the parameter updates during the training and fine-tuning processes. We design the attacks in the stand-alone and federated settings, with respect to passive and active inference attackers, and assuming different adversary prior knowledge. We design and evaluate our novel white-box membership inference attacks against deep learning algorithms to measure their training data membership leakage. We show that a straightforward extension of the known black-box attacks to the white-box setting (through analyzing the outputs of activation functions) is ineffective. We therefore design new algorithms tailored to the white-box setting by exploiting the privacy vulnerabilities of the stochastic gradient descent algorithm, widely used to train deep neural networks. We show that even well-generalized models are significantly susceptible to white-box membership inference attacks, by analyzing state-of-the-art pre-trained and publicly available models for the CIFAR dataset. We also show how adversarial participants of a federated learning setting can run active membership inference attacks against other participants, even when the global model achieves high prediction accuracies.
Emotion Estimation in Crowds: The Interplay of Motivations and Expectations in Individual Emotions

Providing an estimation of the emotional states of individuals increases the insights on the state of a crowd beyond simple normal/abnormal situations or behaviour classification. Methods intended for identifying emotions in individuals are mainly based on facial and body expressions, or even physiological measurements which are not suited for crowded environments as the available information in crowds is usually limited to that provided by surveillance cameras where the face and body of pedestrians can often suffer from occlusion. This work proposes an approach for analysing walking behaviour and exploiting the interplay of motivations and expectations in the emotions of pedestrians. Real-world data is used to test the prediction of motivations and annotations on the emotional state of pedestrians are added to evaluate the proposed method's capability to estimate emotional states. The conducted experiments show significant improvements over previous methods for estimating motivations and consistent results to the estimation of emotions.
Calculating the Influence of Tagging People on Sentiment Analysis

Social media makes it possible for anyone to share and transmit their opinions and sentiments to the rest of the world via the Internet. Recently, sentiment analysis is being used to investigate the opinions posted on Online Social Networks(OSN) based on multiple inputs, such as user profile characteristics, slang, emoticons, among others. However, the current sentiment analysis tools do not consider the influence of tagging people on OSN. In this context, this paper analyzes the impact of the tagging parameter on the global sentiment score of a text. The experimental results of subjective tests show that a correction factor must be considered in case of tagging people. Experimental results demonstrate that the tagging parameter affects the sentiment intensity value, in different ways, depending on the gender of the person who wrote the text and the sentiment polarity of the text. The new sentiment intensity metric considering the tagging people parameter reaches a Pearson Correlation Coefficient of 0.93 and a maximum error of 0.07, for texts of negative polarity written by women, at a 5-point scale. Furthermore, a mobile application with the new sentiment metric, which considers the tagging parameter, is built.
A Bag-of-Phonetic-Codes Modelfor Cyber-Bullying Detection in Twitter

Social networking sites such as Twitter, Facebook, MySpace, Instagram are emerging as a strong medium of communication these days. These have become a part and parcel of daily life. People can express their thoughts and activities among their social circle with brings them closer to their community. However this freedom of expression has its drawbacks. Sometimes people show their aggression on Social Media which in turn hurts the sentiments of the targeted victims. Certain forms of cyber-bullying are sexual, racial and physical disability based. Hence a proper surveillance is necessary to tackle such situations. Twitter as a micro-blogging site sees cyber abuse on a daily basis. However, tweets are raw texts; containing a lot of misspelled words and censored words. This paper proposes a novel method to detect cyber-bullying, a Bag-of-Phonetic-Codes model. Using pronunciation of words as features can rectify misspelled words and can identify censored words. Correctly identifying duplicate words can lead to smaller vocabulary of words, thereby reducing the feature space. The inspiration for this proposed work is drawn from the famous Bag-of-Words model for extracting textual features. Phonetic code generation has been done using the Soundex Algorithm. Besides the proposed model, experiments were carried out with both supervised and unsupervised machine learning approaches on multiple datasets to understand the approaches and challenges in the domain of cyber-bullying detection.
Automatic Segmentation of Kidney and Renal Tumor in CT Images Based on 3D Fully Convolutional Neural Network with Pyramid Pooling Module

Renal cancer is one of ten most common cancers in human beings. The laparoscopic partial nephrectomy (LPN) becomes the main therapeutic approach in treating renal cancer. Accurate kidney and tumor segmentation in CT images is a prerequisite step in the surgery planning. However, automatic and accurate kidney and renal tumor segmentation in CT images remains a challenge. In this paper, we propose a new method to perform a precise segmentation of kidney and renal tumor in CT angiography images. This method relies on a three-dimensional (3D) fully convolutional network (FCN) which combines a pyramid pooling module (PPM). The proposed network is implemented as an end-to-end learning system directly on 3D volumetric images. It can make use of the 3D spatial contextual information to improve the segmentation of the kidney as well as the tumor lesion. The experiments conducted on 140 patients show that these target structures can be segmented with a high accuracy. The resulting average dice coefficients obtained for kidney and renal tumor are equal to 0.931 and 0.802 respectively. These values are higher than those obtained from the other two neural networks.
Will Artificial Intelligence Automate Theory Building? Are there Lessons for Academia from Practice?

To stay competitive, organizations are being challenged to gather vast amounts of data generated from daily operations and customer interactions. Managers have shifted from making decisions that are supported by information systems reporting and intuition to making decisions that are driven by evidence from data. With the lower cost of acquiring data, the proliferation of easy to use data analytics tools and demands for rapid action, firms struggle whether to wait for academia to endorse scholarly theories or to adopt data-driven decisions. This panel serves as a conversation on a way forward for IS researchers. The panelists represent industry experts who can provide their perspective from large data driven companies in Silicon Valley and scholars that have both theoretical and practical research experience in theory development, IT value and adoption, big data and design science
Akademik Öz-Yeterlik ile Akademik Başarı Arasındaki İlişkide Sınav Kaygısı ve Akademik Motivasyonun Aracı Rolü

Bu calismada akademik oz-yeterlik ile akademik basari arasindaki iliskide akademik motivasyonun ve sinav kaygisinin aracilik rollerinin incelenmesi amaclanmistir. Calisma grubu Trabzon ilindeki liselerde ogrenim goren, 186’si kiz, 201’i erkek olmak uzere toplam 387 ogrenciden olusmaktadir. Veri toplama araclari olarak Akademik Oz-Yeterlik Olcegi, Akademik Gudulenme Olcegi ve Revize Edilmis Sinav Kaygisi Olcegi kullanilmistir. Veriler SPSS ve AMOS istatistik paket programlari ile analiz edilmistir. Korelasyon analizlerinin sonucunda, arastirmada yer alan tum degiskenler arasinda anlamli iliskiler oldugu bulunmustur. Yol analizinden elde edilen bulgular akademik motivasyonun ve sinav kaygisinin, akademik oz-yeterlik ile akademik basari arasindaki iliskide kismi aracilik rolunun oldugunu gostermektedir. Arastirmanin sonuclari alan yazindaki teorik aciklamalarin ve benzer arastirmalarin isiginda tartisilmistir.
Photolysis of a Caged, Fast-Equilibrating Glutamate Receptor Antagonist, MNI-Caged γ-D-Glutamyl-Glycine, to Investigate Transmitter Dynamics and Receptor Properties at Glutamatergic Synapses

Fast uncaging of low affinity competitive receptor antagonists can in principle measure the timing and concentration dependence of transmitter action at receptors during synaptic transmission. Here, we describe the development, synthesis and characterization of MNI-caged γ-D-glutamyl-glycine (γ-DGG), which combines the fast photolysis and hydrolytic stability of nitroindoline cages with the well-characterized fast-equilibrating competitive glutamate receptor antagonist γ-DGG. At climbing fiber-Purkinje cell (CF-PC) synapses MNI-caged-γ-DGG was applied at concentrations up to 5 mM without affecting CF-PC transmission, permitting release of up to 1.5 mM γ-DGG in 1 ms in wide-field flashlamp photolysis. In steady-state conditions, photoreleased γ-DGG at 0.55–1.7 mM inhibited the CF first and second paired EPSCs by on average 30% and 60%, respectively, similar to reported values for bath applied γ-DGG. Photolysis of the L-isomer MNI-caged γ-L-glutamyl-glycine was ineffective. The time-course of receptor activation by synaptically released glutamate was investigated by timed photolysis of MNI-caged-γ-DGG at defined intervals following CF stimulation in the second EPSCs. Photorelease of γ-DGG prior to the stimulus and up to 3 ms after showed strong inhibition similar to steady-state inhibition; in contrast γ-DGG applied by a flash at 3–4 ms post-stimulus produced weaker and variable block, suggesting transmitter-receptor interaction occurs mainly in this time window. The data also show a small and lasting component of inhibition when γ-DGG was released at 4–7 ms post stimulus, near the peak of the CF-PC EPSC, or at 10–11 ms. This indicates that competition for binding and activation of AMPA receptors occurs also during the late phase of the EPSC, due to either delayed transmitter release or persistence of glutamate in the synaptic region. The results presented here first show that MNI-caged-γ-DGG has properties suitable for use as a synaptic probe at high concentration and that its photolysis can resolve timing and extent of transmitter activation of receptors in glutamatergic transmission.
A Case Study on Granularity of Industrial Vision Services

Software engineering paradigms such as service-oriented architectures are increasingly often applied in the field of factory automation. Functions like robot motion planning or object recognition are provided by cloud services. A crucial architectural aspect is the granularity, i.e. the scope and size of individual services. In our case study, we examine a service-based object recognition application for a robotic assembly use case. We implement three different granularity levels, measure their communication and computation times and discuss further architectural features. The fine-granular approach encapsulates individual image processing operations as services, which have high reusability but impose large communication overheads. The medium granularity approach is object-wise and offers best reuse efficiency and cohesion. The coarse solution offers the best performance.
Pro-angiogenic Role of Danqi Pill Through Activating Fatty Acids Oxidation Pathway Against Coronary Artery Disease

Coronary artery disease (CAD) is one of the leading causes of deaths worldwide. Energy metabolism disorders, including a reduction in fatty acids oxidation and upregulation of glycolysis pathway, are involved in the process of CAD. Therapeutic angiogenesis has become a promising treatment for CAD. Traditional Chinese medicines, such as Danqi Pill (DQP), have been proven to be effective in treating CAD in China for many years. However, the pro-angiogenic effects of DQP based on fatty acids oxidation are still unknown and the mechanism is worthy of investigation. In this study, left anterior descending (LAD) coronary artery was ligated to induce the CAD models in vivo, and cardiac functions were examined using echocardiography. Human umbilical vein endothelial cells (HUVEC) were subjected to H2O2-induced oxidative stress in vitro. The effects of DQP on CAD rat models and in vitro HUVEC were detected. Our results showed that DQP had cardio-protective effects in rat model. The intensity of capillaries in the marginal area of infarction of the rat heart was increased remarkably in DQP group, and the expression of PPARα and VEGF-2 were increased. The key enzymes involved in the transportation and intake of fatty acids, including CPT1A and CD36, both increased. In H2O2-induced endothelial cells injury models, DQP also showed protective roles and promoted capillary-like tube formation. DQP up-regulated key enzymes in fatty acids oxidation in H2O2-treated HUVEC. In addition, inhibition of CPT1A compromised the pro-angiogenic effects of DQP. In conclusion, fatty acids oxidation axis PPARα-CD36-CPT1A was involved in the pro-angiogenic roles of DQP against CAD. Cardiac CPT1A may serve as a target in therapeutic angiogenesis in clinics.
Mechanisms of AMPA Receptor Endosomal Sorting

The regulation of synaptic AMPA receptors (AMPARs) is critical for excitatory synaptic transmission, synaptic plasticity and the consequent formation of neural circuits during brain development and their modification during learning and memory processes. The number of synaptic AMPARs is regulated through endocytosis, exocytosis and endosomal sorting that results in recycling back to the plasma membrane or degradation in the lysosome. Hence, endo-lysosomal sorting is vitally important in maintaining AMPAR expression at the synapse, and the dynamic regulation of these trafficking events is a key component of synaptic plasticity. A reduction in synaptic strength such as in long-term depression (LTD) involves AMPAR sorting to lysosomes to reduce synaptic AMPAR number, whereas long-term potentiation (LTP) involves an increase in AMPAR recycling to increase the number of AMPARs at synapses. Here, we review our current understanding of the endosomal trafficking routes taken by AMPARs, and the mechanisms involved in AMPAR endosomal sorting, focussing on the numerous AMPAR associated proteins that have been implicated in this complex process. We also discuss how these events are dysregulated in brain disorders.
Applying Topic Segmentation to Document-Level Information Retrieval

In the present paper we discuss how text segmentation could be applied in the information retrieval domain. We assume that topic text segmentation allows one to better model text structure and therefore language itself, which influences the quality of text representation. We test the initial hypothesis by conducting experiments with several baseline models on the arXiv dataset comparing their quality on whole texts and on segmented texts. The experiments demonstrated that, indeed, the quality of retrieval is generally slightly improved.
Secure Out-of-band Remote Management of Virtual Machines with Transparent Passthrough

Infrastructure-as-a-Service clouds provide out-of-band remote management for users to access their virtual machines (VMs). Out-of-band remote management is a method for indirectly accessing VMs via their virtual devices. While virtual devices running in the virtualized system are managed by cloud operators, not all cloud operators are always trusted in clouds. To prevent information leakage from virtual devices and tampering with their I/O data, several systems have been proposed by trusting the hypervisor in the virtualized system. However, they have various issues on security and management. This paper proposes VSBypass, which enables secure out-of-band remote management outside the virtualized system using a technique called transparent passthrough. VSBypass runs the entire virtualized system in an outer VM using nested virtualization. Then it intercepts I/O requests of out-of-band remote management and processes those requests in shadow devices, which run outside the virtualized system. We have implemented VSBypass in Xen for the virtual serial console and GUI remote access. We confirmed that information leakage was prevented and that the performance was comparable to that in traditional out-of-band remote management.
Antipsychotics, Metabolic Adverse Effects, and Cognitive Function in Schizophrenia

Cognitive impairment is a core symptom domain of schizophrenia. The effect of antipsychotics, the cornerstone of treatment in schizophrenia, on this domain is not fully clear. There is some evidence suggesting that antipsychotics may partially improve cognitive function, and that this improvement may vary depending on the specific cognitive domain. However, this research is confounded by various factors, such as age, duration/stage of illness, medication adherence, and extrapyramidal side effects that complicate the relationship between antipsychotics and cognitive improvement. Furthermore, antipsychotics—particularly the second generation, or “atypical” antipsychotics—can induce serious metabolic side effects, such as obesity, dyslipidemia and type 2 diabetes, illnesses which themselves have been linked to impairments in cognition. Thus, the inter-relationships between cognition and metabolic side effects are complex, and this review aims to examine them in the context of schizophrenia and antipsychotic treatment. The review also speculates on potential mechanisms underlying cognitive functioning and metabolic risk in schizophrenia. We conclude that the available literature examining the inter-section of antipsychotics, cognition, and metabolic effects in schizophrenia is sparse, but suggests a relationship between metabolic comorbidity and worse cognitive function in patients with schizophrenia. Further research is required to determine if there is a causal connection between the well-recognized metabolic adverse effects of antipsychotics and cognitive deficits over the course of the illness of schizophrenia, as well as, to determine underlying mechanisms. In addition, findings from this review highlight the importance of monitoring metabolic disturbances in parallel with cognition, as well as, the importance of interventions to minimize metabolic abnormalities for both physical and cognitive health.