Miracle-dz/UniFAR-FTUs huggingface dataset:
PUFs Deep Attacks: Enhanced modeling attacks using deep learning techniques to break the security of double arbiter PUFs

In the past decade and a half, physical unclonable functions (PUFs) have been introduced as a promising cryptographic primitive for hardware security applications. Since then, the race between proposing new complex PUF architectures and new attack schemes to break their security has been ongoing. Although modeling attacks using conventional machine learning techniques were successful against many PUFs, there are still some delay-based PUF architectures which remain unbroken against such attacks, such as the double arbiter PUFs. These stronger complex PUFs have the potential to be a promising candidate for key generation and authentication applications. This paper presents an in-depth analysis of modeling attack using deep learning (DL) techniques against double arbiter PUFs (DA-PUFs). Unlike more conventional machine learning techniques such as logistic regression and support vector machines, DL results show enhanced prediction accuracy of the attacked PUFs, thus pushing up the boundaries of modeling attacks to break more complex architectures. The attack on 3-1 DAPUFs has improved accuracy of over 86% (compared to previous research achieving a maximum of 76%) and the 4-1 DAPUFs accuracy ranges between 71%-81.5% (compared to previous research of maximum 63%). This research is crucial for analyzing security of existing and future PUF architectures, confirming that as DL computations become more widely accessible, designers will need to hide the PUFs CRP relationship from attackers.
Serum ferritin as a tumor marker in patients with squamous cell carcinoma of the head and neck

A double antibody enzyme immunoassay was used to measure serum ferritin levels in several different control and tumor‐bearing populations collected from two institutions. The control groups consisted of normal volunteers, smokers, and Latter Day Saints. No statistically significant differences were noted in ferritin levels between pairs of these groups. Differences were noted among the normal groups when separated on the basis of age and sex, with higher ferritin levels in individuals older than 32 years of age and in men. By one‐way analysis of variance, most control groups and subgroups were shown to have significantly lower levels (P < 0.05) than the head and neck cancer group, with the exception of male smokers, who had levels comparable to male head and neck cancer patients. Serum ferritin levels were higher in head and neck cancer patients than in controls; however, there was no difference when compared with patients with other types of solid malignancies or when considering the anatomic site of the head and neck lesion. Ferritin levels were significantly (P < 0.05) higher in patients with advanced (Stages III and IV) cancer than in those individuals with Stage I or II cancer. In patients with no evidence of clinical disease 5 years after treatment, the ferritin level had essentially returned to normal. In a group of head and neck cancer patients followed longitudinally, a significant decline in ferritin levels (P < 0.05) was seen by 5 months after the completion of successful treatment. Furthermore, ferritin levels showed a tendency to increase or remain at high levels in patients with a poor prognosis and to decrease in those patients with a favorable prognosis, giving support to the contention that ferritin may prove to be a valuable tumor marker in head and neck cancer.
An Effective Model-Based Integration Testing Technique for Component-Based Software

Recently, many issues and challenges have been identified in integration testing of current Component-Based Software Systems (CBSS), that lead to ineffective testing and, ultimately, to CBSS of poor quality. In this research, a model-based integration testing technique for CBSS has been proposed by combining model learning and testing techniques for the formal analysis and testing of a system of integrated black-box software components. The effectiveness of the proposed technique is evaluated by applying a case study, namely, the elevator system. The performance analysis is carried out by performing a comparative analysis of proposed technique and existing techniques by using three evaluation parameters. The results of the comparison showed that the proposed technique is effective and outperforms the existing well-known technique in terms of learned components that performance is 100% better, output queries that performance is 25.01% better, as well as error detection capability that is 75%.
Real-Time Detection of Apple Leaf Diseases Using Deep Learning Approach Based on Improved Convolutional Neural Networks

Alternaria leaf spot, Brown spot, Mosaic, Grey spot, and Rust are five common types of apple leaf diseases that severely affect apple yield. However, the existing research lacks an accurate and fast detector of apple diseases for ensuring the healthy development of the apple industry. This paper proposes a deep learning approach that is based on improved convolutional neural networks (CNNs) for the real-time detection of apple leaf diseases. In this paper, the apple leaf disease dataset (ALDD), which is composed of laboratory images and complex images under real field conditions, is first constructed via data augmentation and image annotation technologies. Based on this, a new apple leaf disease detection model that uses deep-CNNs is proposed by introducing the GoogLeNet Inception structure and Rainbow concatenation. Finally, under the hold-out testing dataset, using a dataset of 26,377 images of diseased apple leaves, the proposed INAR-SSD (SSD with Inception module and Rainbow concatenation) model is trained to detect these five common apple leaf diseases. The experimental results show that the INAR-SSD model realizes a detection performance of 78.80% mAP on ALDD, with a high-detection speed of 23.13 FPS. The results demonstrate that the novel INAR-SSD model provides a high-performance solution for the early diagnosis of apple leaf diseases that can perform real-time detection of these diseases with higher accuracy and faster detection speed than previous methods.
Single Image Super Resolution Model Using Learnable Weight Factor in Residual Skip Connection

In single image super-resolution problems, the recent feed forward deep learning architectures use residual connections in order to preserve local features and carry them through the next layer. In a simple residual skip connection, all the features of the earlier layer are concatenated with the features of the current layer. A simple concatenation of the features does not exploit the fact that some features may be more useful than other features and vice versa. To overcome this limitation, we propose an extended architecture (baby neural network) which will have input as the features learned from the previous layer and output a multiplication factor. This multiplication factor will give importance to the given feature and thus help in training the current layer’s features more accurately. The proposed model clearly outperforms the existing works.
A Runtime Resource Management Policy for OpenCL Workloads on Heterogeneous Multicores

Nowadays, runtime workload distribution and resource tuning for heterogeneous multicores running multiple OpenCL applications is still an open quest. This paper proposes an adaptive policy capable at identifying an optimal working point for an unknown multiprogrammed OpenCL workload without using any design-time application profiling or analysis. The approach compared against a design-time optimization strategy demonstrates to be effective in converging to an solution guaranteeing required performance while minimizing power consumption and maximum temperature; it achieves on average values 0.085 W (5.15%) and 0.83°C (1.47%) worse than the static optimal solution.
Multi-objective Precision Optimization of Deep Neural Networks for Edge Devices

Precision tuning post-training is often needed for efficient implementation of deep neural networks especially when the inference implementation platform is resource constrained. While previous works have proposed many ad hoc strategies for this task, this paper describes a general method for allocating precision to trained deep neural networks data based on a property relating errors in a network. We demonstrate that the precision results of previous works for hardware accelerator or understanding cross layer precision requirement is subsumed by the proposed general method. It has achieved a 29% and 46% energy saving over the state-of-the-art search-based method for GoogleNet and VGG-19 respectively. Proposed precision allocation method can be used to optimize for different criteria based on hardware design constraints, allocating precision at the granularity of layers for very deep networks such as Resnet-152, which hitherto was not achievable.
The Ramifications of Making Deep Neural Networks Compact

The recent trend in deep neural networks (DNNs) research is to make the networks more compact. The motivation behind designing compact DNNs is to improve energy efficiency since by virtue of having lower memory footprint, compact DNNs have lower number of off-chip accesses which improves energy efficiency. However, we show that making DNNs compact has indirect and subtle implications which are not well-understood. Reducing the number of parameters in DNNs increases the number of activations which, in turn, increases the memory-footprint. We evaluate several recently-proposed compact DNNs on Tesla P100 GPU and show that their "activations to parameters ratio" ranges between 1.4 to 32.8. Further, the " memory-footprint to model size ratio" ranges between 15 to 443. This shows that a higher number of activations causes large memory footprint which increases on-chip/off-chip data movements. Furthermore, these parameter-reducing techniques reduce the arithmetic intensity which increases on-chip/off-chip memory bandwidth requirement. Due to these factors, the energy efficiency of compact DNNs may be significantly reduced which is against the original motivation for designing compact DNNs.
An Analytic Approach to Nonlinearity Analysis of Memristor

The nonlinearity characteristics of memristor have been widely employed in artificial intelligence. However, existing studies do not provide much attention to the analytic analysis of nonlinearity. This paper presents an analytic approach to the nonlinearity of memristor by analyzing the output spectrum based on homotopy analysis method (HAM). The analytic spectrum expressions obtained by this approach are verified by the fingerprints of memristor. It can be simultaneously revealed that one of the potential sources of the nonlinearity is the large variation of state variable. Furthermore, we propose a mapping method based on the obtained total harmonic distortion (THD) and a new parameter—threshold frequency <inline-formula> <tex-math notation="LaTeX">$\omega _{C}$ </tex-math></inline-formula> to investigate the relationship between the memristor transition (i.e., the transition of the memristor between a nonlinear resistor and a linear resistor) and the frequency of input signal. Especially, analytic output spectrum, THD, and threshold frequency <inline-formula> <tex-math notation="LaTeX">$\omega _{C}$ </tex-math></inline-formula> can be solved without complex mathematical operations and, thus, compared with traditional works, the complexity of the proposed approach is reduced.
Design of Hardware-Friendly Memory Enhanced Neural Networks

Neural networks with external memories have been proven to minimize catastrophic forgetting, a major problem in applications such as lifelong and few-shot learning. However, such memory enhanced neural networks (MENNs) often require a large number of floating point-based cosine distance metric calculations to perform necessary attentional operations, which greatly increases energy consumption and hardware cost. This paper investigates other distance metrics in such neural networks in order to achieve more efficient hardware implementations in MENNs. We propose using content addressable memories (CAMs) to accelerate and simplify attentional operations. Our hardware friendly approach implements fixed point L∞ distance calculations via ternary content addressable memories (TCAM) and fixed point L1 and L2 distance calculations on a general purpose graphical processing unit (GPGPU). As a representative example, a 32-bit floating point-based cosine distance MENN with M • D multiplications has a 99.06% accuracy for the Omniglot 5-way 5-shot classification task. Based on our approach, with just 4-bit fixed point precision, a L∞- L1 distance hardware accuracy of 90.35% can be achieved with just 16 TCAM lookups and 16•D addition and subtraction operations. With 4-bit precision and a L∞-L2 distance, hardware classification accuracies of 96.00% are possible. Hence, 16 TCAM lookups and 16•D multiplication operations are needed. Assuming the hardware memory has 512 entries, the number of multiplication operations is reduced by 32x versus the cosine distance approach.
Clinical and lung-function variables associated with vocal cord dysfunction.

BACKGROUND Vocal cord dysfunction (VCD) is difficult to diagnose. Laryngoscopy while the patient is symptomatic is the accepted standard method to establish a diagnosis of VCD, but patient characteristics and spirometry values are thought to be useful for predicting VCD. We sought to identify clinical and spirometric variables that suggest VCD. METHODS We performed 2 parallel studies. First, 3 staff pulmonologists (who were blinded to the laryngoscopy results), scored 3 flow-volume loops from each PFT session on the likelihood that the inspiratory curve indicated VCD. We also performed a cross-sectional study of clinical characteristics and spirometric data from all patients who underwent laryngoscopy for any indication, including suspected VCD, over a 3-year period. We compared the laryngoscopy findings to the clinical characteristics, spirometry results, and the pulmonologists' assessments of the flow-volume loops. We used multivariate logistic regression to identify independent predictors of VCD. RESULTS The pulmonologists agreed about which flow-volume loops predicted VCD (quadratic kappa range 0.55-0.76), but those ratings were not predictive of laryngoscopic diagnosis of VCD. During the study period, 226 patients underwent laryngoscopy. One hundred (44%) were diagnosed with VCD. Independent predictors of VCD included female sex (odds ratio 2.72, 95% confidence interval 1.55-4.75) and obesity (body mass index > 30 kg/m(2)) (odds ratio 2.06, 95% confidence interval 1.12-3.80). With spirometric data from the effort that had the best forced-vital-capacity, multivariate analysis found the ratio of the forced inspiratory flow at 25% of the inspired volume to forced inspiratory flow at 75% of the inspired volume (FIF(25%/75%)) predictive of VCD (odds ratio 1.97, 95% confidence interval 1.12-3.44). The diagnostic performance of these characteristics was poor; the area under the receiver-operating-characteristic curve was 0.68. With the spirometric data from the effort that had the subjectively determined best inspiratory curve, and after controlling for the reproducibility of the inspiratory curves, multivariate analysis found none of the spirometric variables predictive of VCD. CONCLUSIONS VCD remains difficult to predict with spirometry or flow-volume loops. If VCD is suspected, normal flow-volume loop patterns should not influence the decision to perform laryngoscopy.
Child sexual and physical abuse and alcoholism: a review.

OBJECTIVE To examine possible relations between child sexual or physical abuse and adult alcoholism. METHOD Studies reviewed included prospective studies, retrospective studies on the prevalence of child sexual or physical abuse in alcoholics and nonalcoholics, and retrospective studies in mental health clients and in population samples comparing the prevalence of alcohol use disorders in abused and nonabused subjects. RESULTS Prospective studies do not indicate a significant association between child sexual or physical abuse and alcoholism. In contrast, studies among alcoholic women do suggest a relationship. Also, a significantly higher prevalence of alcohol problems in abused women than in nonabused women is found in population samples. The results of studies among mental health clients are inconclusive. In addition, several methodological limitations should be taken into consideration when evaluating results of the available studies. CONCLUSIONS Current evidence is insufficient to draw conclusions about relationships between child sexual or physical abuse and alcoholism among men. Among females, however, there is a higher likelihood of alcohol problems if they were sexually or physically abused as children.
Characteristics of cranial ultrasound white‐matter echolucencies that predict disability: a review

Cranial ultrasound abnormalities, especially a white-matter echolucency, predict disability (Paneth and PintoMartin 1990, Whitaker et al. 1990, Dammann and Leviton 1997), but with limitations. For example, half of very preterm infants who develop cerebral palsy (CP) do not have any abnormality of the white matter (Pinto-Martin et al. 1995, O’Shea et al. 1998). In addition, echolucencies that predict spastic CP are seldom located with descending fibers from the motor cortex (Rogers et al. 1994). These findings have led to the ‘tip of the iceberg’ hypothesis which postulates that cranial ultrasound scans do not detect the entirety of the white-matter damage (Leviton and Gilles 1996). Correlating ultrasound with magnetic resonance images (Huppi et al. 1996, Battin et al. 1998) might provide the best assessment of the ‘tip of the iceberg’ hypothesis. However, as such studies are presently unavailable, we evaluated the hypothesis by exploring structure–function relations using echolucency as the sonographic indicator of white-matter destruction. We prefer the term echolucency to the diagnosis of cystic periventricular leukomalacia because it is descriptive, does not imply histological characteristics, and because we are not able to link every echolucency to a diagnosis of either periventricular leukomalacia or periventricular hemorrhagic infarction (Kuban 1998) using conventional criteria (Volpe 1989). We evaluated 15 studies that satisfied the following criteria: details about the size, extent or location of the cerebral white-matter echolucencies seen on cranial ultrasound scans, follow-up assessment (i.e. motor, cognitive, behavioral, sensory, and so on) related to echolucency characteristics, and a sample size of at least 10 infants (Calame et al. 1985, Calvert et al. 1986, Graziani et al. 1986, Cooke 1987, Graham et al. 1987, Monset-Couchard et al. 1988, Shortland et al. 1988, Zorzi et al. 1988, Fawer and Calame 1991, Ikonen et al. 1992, de Vries et al. 1993, Ringelberg and van de Bor 1993, Fazzi et al. 1994, Fujimoto et al. 1994, Rogers et al. 1994). The reports varied considerably in the detail about location and size of echolucencies, and performance at the time of follow-up assessment. To maximize comparability we tried to allocate the terms used in each report to a common set of descriptors. The descriptors for echolucencies were frontal, frontal-parietal, parietal, parietal-occipital, occipital, frontal-parietal-occipital, unilateral, bilateral, singular, multiple, small, moderate, and large. The outcome descriptors were any CP diagnosis, diplegia, hemiplegia, quadriplegia, mental retardation (IQ or DQ<70), vision impairment, hearing impairment, and severe behavioral problems. Despite the lack of uniformity among the reviewed studies, we pooled the results from all 15 studies. A total of 1384 infants were followed. Of 373 infants diagnosed with echolucencies, only 59% developed CP. Conversely, CP developed in only 3.0% of the 795 infants who had normal head ultrasound scans.
Multiple Source Phototherapy in Breast Cancer: A Viability Study

Radiation therapy is one of many common treatments applied to breast cancer. Most usual radiation sources applied are ionizing radiation, such as g-rays and X-rays, and non-ionizing radiation such as ultraviolet radiation. The possibility of using near infrared light to photoactivate a drug inside an 8 cm diameter biological object is discussed in this work via Monte Carlo simulations. Two simulation setups performed in the Geant4/GAMOS framework are presented in order to study the viability of photoactivating a drug by using several near infrared light sources. The overall objective of this technique is to minimize energy concentrated at objects surface and maximize it in a predefined region of interest. Results show an increase energy absorption in the desired region of interest inside a 8 cm object, when a higher absorption particle is present. With the use of multiple sources it is possible to photoactivate the drug while causing minimal damage to the surface of the radiated object.
Clozapine v. conventional antipsychotic drugs for treatment-resistant schizophrenia: A re-examination

Background Although there is a consensus that clozapine is more effective than conventional antipsychotic drugs for treatment-resistant schizophrenia, there is great heterogeneity among results of relevant trials. Aims To re-evaluate the evidence comparing clozapine with conventional antipsychotics and to investigate sources of heterogeneity. Method Individual studies were inspected with assessment of clinical relevance of results. Meta-regression analysis was performed to investigate sources of heterogeneity. Results Ten trials were examined. Recent large-scale studies have not found a substantial advantage for clozapine, especially in terms of a clinically relevant effect. Meta-regression showed that shorter study duration, financial support from a drug company and higher baseline symptom score consistently predicted greater advantage of clozapine. Conclusions It may be inappropriate to combine studies in meta-analysis, given the degree of heterogeneity between their findings. The benefits of clozapine compared with conventional treatment may not be substantial.
Market, Hierarchy, and Community as Organizing Principles in Knowledge-Intensive Work

While firms are under increasing pressure to more quickly innovate with fewer resources as a result of financialization, they have increasingly adopted formal management control measures for product development as well as performance monitoring and performance-based incentives for engineers. However, it is unclear how these formal control measures impact engineers' behavior and capability development, which we examine in this paper based on interviews and a questionnaire survey of Japanese engineers. The result is that formal, strict management control and performance-based incentives almost negatively correlate with engineers' behaviors that promote innovation, while sharing organizational goals, values, and norms positively correlates with these behaviors. Following Polanyi, market, hierarchy, and community are three principles by which to organize economic activities. Thus, our result implies the "crowding out" of the positive impact of community principle by the negative impact of market and hierarchy principles in response to the growing pressure as above.