Search

The top online marketplaces in the world, such as Alibaba, Amazon, eBay and others, sold $2.7 trillion in 2020, or 62% of global web sales that year. Despite the promise of unfettered access to customers, the reality is that most sellers struggle with unprecedented unauthorized “rogue resellers.” These often anonymous, third party individuals and firms may not provide the quality experiences aligned with customers’ expectations of a brand and often market their goods in competition with trusted partners. This requires a systematic battle plan in order to effectively protect the seller’s brand equity, service promise and authorized distribution network. We describe the channel war that is at work and the forces fueling its development. We explain how rogue reseller activities drive down pricing, increase channel competition and conflict, impact customer experience, and harm brand equity. We recommend revising product policies, procedures and agreements to begin to differentiate products from those sold by unauthorized parties. We also recommend a legal strategy to complement these efforts. Critical steps include regular and active enforcement, as well as implementing services, benefits and quality controls for the brand’s products that cannot be replicated by unauthorized sellers and requiring authorized distributors and retailers to maintain those benefits and controls.

Our previous work selected for Top Picks in Hardware and Embedded Security 2020 demonstrates that it is possible to reverse engineer neural networks by using side-channel attacks. We developed a framework that considers each part of the neural network separately and then, by combining the information, manages to reverse engineer all relevant hyper-parameters and parameters. Our work is a proof of concept (but also a realistic demonstration) that such attacks are possible and warns that more effort should be given to developing countermeasures. While we have used microcontrollers for our experiments, the attack applies to other targets like FPGAs and GPUs. National Research Foundation (NRF) Accepted version This research is partly supported by the National Research Foundation, Singapore, under its National Cybersecurity Research & Development Programme / Cyber-Hardware Forensic & Assurance Evaluation R&D Programme (Award: NRF2018NCR-NCR009-0001)

Franchise encroachment, or the addition of an outlet in the vicinity of existing franchisees, is largely viewed as resulting in revenue cannibalization of incumbent locations. Against this backdrop, the authors consider the possibility that the addition of same brand outlets can, in fact, also create positive effects via customer utility and ultimately benefit franchisees, due to a range of mechanisms such as quality signaling, learning, or brand awareness, resulting in a positive pathway on franchisee performance. The authors unpack this possibility using an experiment and detailed proprietary and publicly available data sets from the hotel industry over a five-year period. Their results show evidence of positive effects on customer utility for same-brand outlets and stronger effects for newer brands, cross brands, and online travel agency channel bookings. Counterfactual simulations indicate that although encroachment hurts franchisees on average, it can modestly benefit same-brand franchisees in low-brand-density markets. Together, the findings illustrate the potential “sunny side” of encroachment, underscoring the need to update our view of encroachment as context-dependent. The novel emphasis on customers versus the dominant firm view suggests customer and incumbent responses to encroachment should be accounted for in the development of franchise strategy and public policy decisions.

Throughout a distribution channel, multiple sales platforms and technologies are leveraged to expand market reach and manage offering assortments. Yet, the literature is limited on how buyers who are channel intermediaries make purchase decisions across sales platforms particularly when platforms vary in their degree of technology enablement. This research investigates how B2B buyers respond to the offering, in terms of numerical assortment and quality, when the sales environments vary in their level of technology enablement. We examine the buyer's sales platform choice, purchase quantity, and tackle the seller's challenge of assortment integration. Buyers have a choice of purchasing from a low-tech platform where buyers are in-person and an auction determines sales, an Internet-enabled platform where remotely located buyers are price takers, and a hybrid platform that has a high level of technology enablement such that co-located and remotely located potential buyers simultaneously bid on the offering. A Hidden Markov Model captures the impact of different need states on buyers' decisions. Our findings: (1) uncover three distinct buyer profiles that have unique sales platform preferences and multiplatform buying tendencies that relate to their comfort with technology-enabled markets, (2) show that buyers exhibit distinct and dynamic needs that affect their sales platform choices and response to the platform offerings, (3) reveal that buyers experience diminishing returns to product assortment and condition at varying rates depending on their need state. Supporting our key findings, we find evidence of similar behaviors among consumers in a B2C multichannel context that includes an omnichannel. Additionally, we offer recommendations for B2B sellers regarding allocating and adjusting assortments to drive sales platform choice and revenues per buyer.

Background: No evidence currently exists characterising global outcomes following major cancer surgery, including esophageal cancer. Therefore, this study aimed to characterise impact of high income countries (HIC) versus low and middle income countries (LMIC) on the outcomes following esophagectomy for esophageal cancer.Method: This international multi-center prospective study across 137 hospitals in 41 countries included patients who underwent an esophagectomy for esophageal cancer, with 90-day follow-up. The main explanatory variable was country income, defined according to the World Bank Data classification. The primary outcome was 90-day postoperative mortality, and secondary outcomes were composite leaks (anastomotic leak or conduit necrosis) and major complications (Clavien-Dindo Grade III - V). Multivariable generalized estimating equation models were used to produce adjusted odds ratios (ORs) and 95% confidence intervals (CI95%).Results: Between April 2018 to December 2018, 2247 patients were included. Patients from HIC were more significantly older, with higher ASA grade, and more advanced tumors. Patients from LMIC had almost three-fold increase in 90-day mortality, compared to HIC (9.4% vs 3.7%, p < 0.001). On adjusted analysis, LMIC were independently associated with higher 90-day mortality (OR: 2.31, CI95%: 1.17-4.55, p = 0.015). However, LMIC were not independently associated with higher rates of anastomotic leaks (OR: 1.06, CI95%: 0.57-1.99, p = 0.9) or major complications (OR: 0.85, CI95%: 0.54-1.32, p = 0.5), compared to HIC.Conclusion: Resections in LMIC were independently associated with higher 90-day postoperative mortality, likely reflecting a failure to rescue of these patients following esophagectomy, despite similar composite anastomotic leaks and major complication rates to HIC. These findings warrant further research, to identify potential issues and solutions to improve global outcomes following esophagectomy for cancer.

Over the years, deep learning algorithms have advanced a lot and any innovation in the algorithms are demonstrated and benchmarked for image classification. Several other field including side-channel analysis (SCA) have recently adopted deep learning with great success. In SCA, the deep learning algorithms are typically working with 1-dimensional (1-D) data. In this work, we propose a unique method to improve deep learning based side-channel analysis by converting the measurements from raw-trace of 1-dimension data based on float or byte data into picture-formatted trace that has information based on the data position. We demonstrate why 'Picturization' is more suitable for deep learning and compare how input and hidden layers interact for each raw (1-D) and picture form. As one potential application, we use a Binarized Neural Network (BNN) learning method that relies on a BNN's natural properties to improve variables. In addition, we propose a novel criterion for attack success or failure based on statistical confidence level rather than determination of a correct key using a ranking system. Published version

Deep learning approaches have become popular for Side-Channel Analysis (SCA) in the recent years. Especially Convolutional Neural Networks (CNN) due to their natural ability to overcome jitter-based as well as masking countermeasures. Most of the recent works have been focusing on optimising the performance on given dataset, for example finding optimal architecture and using ensemble, and bypass the need for trace pre-processing. However, trace pre-processing is a long studied topic and several proven techniques exist in the literature. There is no straightforward manner to integrate those techniques into deep learning based SCA. In this paper, we propose a generic framework which allows seamless integration of multiple, user defined pre-processing techniques into the neural network architecture. The framework is based on Multi-scale Convolutional Neural Networks ( $\mathsf {MCNN}$ ) that were originally proposed for time series analysis. $\mathsf {MCNN}$ are composed of multiple branches that can apply independent transformation to input data in each branch to extract the relevant features and allowing a better generalization of the model. In terms of SCA, these transformations can be used for integration of pre-processing techniques, such as phase-only correlation, principal component analysis, alignment methods, etc . We present successful results on generic network which generalizes to different publicly available datasets. Our findings show that it is possible to design a network that can be used in a more general way to analyze side-channel leakage traces and perform well across datasets.

Recently, renewable power generation and electric vehicles (EVs) have been attracting more and more attention in smart grid. This paper presents a grid-connected solar-wind hybrid system to supply the electrical load demand of a small shopping complex located in a university campus in India. Further., an EV charging station is incorporated in the system. Economic analysis is performed for the proposed setup to satisfy the charging demand of EVs as well as the electrical load demand of the shopping complex. The proposed system is designed by considering the cost of the purchased energy, which is sold to the utility grid, while the power exchange is ensured between the utility grid and other components of the system. The sizing of the component is performed to obtain the least levelized cost of electricity (LCOE) while minimizing the loss of power supply probability (LPSP) by using recent optimization techniques. The results demonstrate that the LCOE and LPSP for the proposed system are measured at 0.038 $/kWh and 0.19% with a renewable fraction of 0.87, respectively. It is determined that a cost-effective and reliable system can be designed by the proper management of renewable power generation and load demands. The proposed system may be helpful in reducing the reliance on the over-burdened grid, particularly in developing countries.

The COVID-19 pandemic disrupted pharmacy education and caused significant transformation in student learning and assessment. Pharmacy PA faculty transformed the traditional PA teaching and practicum to virtual utilising Zoom. PA practicum consists of demonstration of a set of short videos, followed by students identifying incorrect techniques, describing the correct technique, and the purpose of the specific PA skill. After completion of the virtual PA practicum examination, students were invited to complete a 20-item survey about their perceptions. Most students preferred blended learning (56.8%) format over in-class, even though students performed significantly better on the traditional practicum (0.89+0.13) rather than the virtual (0.84+0.11). Hands-on skills are an integral part to PA instruction and learning, making innovations in virtual delivery and assessment vital for student success. This pandemic gave opportunities to explore curricular innovations as our virtual formats provided alternative approaches to review and summative assessment of PA.

Lightweight block ciphers rely on simple operations to allow compact implementation. Thanks to its efficiency, bit permutation has emerged as an optimal choice for state-wise diffusion. It can be implemented by simple wiring in hardware or shifts in software. However, efficiency and security often go against each other. In this paper, we show how bit permutations introduce a side-channel vulnerability that can be exploited to extract the secret key from the cipher. Such vulnerabilities are specific to bit permutations and do not occur in other state-wise diffusion alternatives. We propose side-channel assisted differential-plaintext attack (SCADPA) which targets this vulnerability in the bit permutation operation. SCADPA is first experimentally demonstrated on PRESENT-80 on an 8-bit microcontroller, with the best case key recovery in 17 encryptions. In Addition, we adjust SCADPA to state-of-the-art bit sliced implementation from CHES'17 with experimental evaluation on a 32-bit microcontroller. The attack is then extended to latest bit-permutation-based cipher GIFT, allowing full key recovery in 36 encryptions. Application for reverse engineering of secret S-boxes in PRESENT-like proprietary ciphers is also shown. NRF (Natl Research Foundation, S’pore) Accepted version

Right from its introduction, fault attacks (FA) have been established to be one of the most practical threats to both public key and symmetric key based cryptosystems. Statistical Ineffective Fault Analysis (SIFA) is a recently proposed class of fault attacks introduced at CHES 2018. The fascinating feature of this attack is that it exploits the correct ciphertexts obtained during a fault injection campaign, instead of the faulty ciphertexts. SIFA has been shown to bypass almost all of the existing fault attack countermeasures even when they are combined with masking schemes for side-channel resistance. The goal of this work is to propose a countermeasure framework for SIFA. It has been observed that a randomized domain transformation of the intermediate computation combined with bit-level error correction can prevent SIFA attacks. The domain transformation ( Transform ) can be realized by standard masking schemes. In fact, we prove that if biased faults are injected at the state register of a block cipher at a certain target round, then masking is sufficient for SIFA protection, until all the shares for a specific bit are corrupted. However, masking alone cannot prevent SIFA if the faults are injected at certain specific locations inside the S-Boxes. To address this issue, we incorporate a bit-level error-correction mechanism ( Encode ). An instantiation of this Transform -and- Encode (TaE) framework, called AntiSIFA, has been proposed and realized for the block cipher PRESENT as a proof-of-concept. Practical evaluation of the countermeasure implementation in both hardware and software ensures our theoretical claims regarding SIFA security, as well as protection against Side-Channel-Attacks (SCA).

Background Conducting systematic reviews (“reviews”) requires a great deal of effort and resources. Making data extracted during reviews available publicly could offer many benefits, including reducing unnecessary duplication of effort, standardizing data, supporting analyses to address secondary research questions, and facilitating methodologic research. Funded by the US Agency for Healthcare Research and Quality (AHRQ), the Systematic Review Data Repository (SRDR) is a free, web-based, open-source, data management and archival platform for reviews. Our specific objectives in this paper are to describe (1) the current extent of usage of SRDR and (2) the characteristics of all projects with publicly available data on the SRDR website. Methods We examined all projects with data made publicly available through SRDR as of November 12, 2019. We extracted information about the characteristics of these projects. Two investigators extracted and verified the data. Results SRDR has had 2552 individual user accounts belonging to users from 80 countries. Since SRDR’s launch in 2012, data have been made available publicly for 152 of the 735 projects in SRDR (21%), at a rate of 24.5 projects per year, on average. Most projects are in clinical fields (144/152 projects; 95%); most have evaluated interventions (therapeutic or preventive) (109/152; 72%). The most frequent health areas addressed are mental and behavioral disorders (31/152; 20%) and diseases of the eye and ocular adnexa (23/152; 15%). Two-thirds of the projects (104/152; 67%) were funded by AHRQ, and one-sixth (23/152; 15%) are Cochrane reviews. The 152 projects each address a median of 3 research questions (IQR 1–5) and include a median of 70 studies (IQR 20–130). Conclusions Until we arrive at a future in which the systematic review and broader research communities are comfortable with the accuracy of automated data extraction, re-use of data extracted by humans has the potential to help reduce redundancy and costs. The 152 projects with publicly available data through SRDR, and the more than 15,000 studies therein, are freely available to researchers and the general public who might be working on similar reviews or updates of reviews or who want access to the data for decision-making, meta-research, or other purposes.

The development technology is something that happens quickly and surely this is marked by the number of new manufacturing companies that are emerging and spreading throughout the world. In the manufacturing industry the most important thing that must always be maintained is production efficiency, but the majority of industries only pay attention to the efficiency aspects of their production activities without regard to the aspects of humans who work in the industry. Human resource scorecard (HRSC) is an appropriate measurement method in identifying and measuring the relationship between workers, strategy, and performance to produce a good company. This human resource performance measurement is looking at business units from four perspectives: financial perspective (F), the customer (C), internal business process (I), and learning and growth (L). Each perspective in HRSC weighted by Analytical Hierarchy process method and then calculated by Objective Matrix (OMAX) to identify which category needed an extra attention and need to be repaired. The end result of OMAX will be reviewed by management theory so the solution made in this analysis will produce the best end result that needed by the company. This measurement is very critical to the company because the competition between company is getting tougher nowadays. HRSC measurement was carried in PT OCM by describing the company’s vision and mission into human resource action whose contribution can be measured using AHP analysis in 4 perspectives: financial perspective with a weight of 0.396, customer perspective (employee) with a weight of 0.118, internal business perspective with a weight of 0.240, and growth and development perspective with a weight of 0.247 after that PT OCM use that measurement in OMAX method to determine what KPI needed extra attention and repairment, management based theory will be applied in that solution to give the best result.

Diabetes mellitus (DM) is a metabolic disorder with growing global incidence, as 387 million people were diagnosed in 2014 with an expected projection of 642 million in 2040. Several complications are associated with DM including heart attack, stroke, kidney failure, blindness, and cancer. The latter is the second leading cause of death worldwide accounting for one in every six deaths, with liver, pancreas, and endometrium cancers are the most abundant among patients with diabetes. Phosphoinositide 3-kinase (PI3K)/protein kinase B (Akt)/mammalian target of rapamycin (mTOR) pathway plays a vital role in developing a wide array of pathological disorders, among them diabetes and cancer. Natural secondary metabolites that counteract the deleterious effects of reactive oxygen species (ROS) and modulate PI3K/Akt/mTOR pathway could be a promising approach in cancer therapy. Here, 717 medicinal plants with antidiabetic activities were highlighted along with 357 bioactive compounds responsible for the antidiabetic activity. Also, 43 individual plant compounds with potential antidiabetic activities against cancer via the modulation of PI3K/Akt/mTOR cascade were identified. Taken together, the available data give an insight of the potential of repurposing medicinal plants and/or the individual secondary metabolites with antidiabetic activities for cancer therapy.

In times like these, appreciating the essential nature of palliative and end-of-life care is perhaps more critical than ever. At the time of writing, more than 2.8 million confirmed cases and nearl...

Race and ethnicity play a significant role in poststroke outcomes. This brief report describes the presence of depression among stroke survivors who received inpatient rehabilitation and whether depression differs by race. Data from eRehabData and electronic medical records were analyzed for patients who received rehabilitation after an acute ischemic or hemorrhagic stroke. Of 1501 stroke patients, 61.3% were white, 33.9% were African American, and 4.8% were of other race/ethnic backgrounds. By retrospective clinical review, depression was documented for 29.7% of stroke patients. Premorbid versus new onset of poststroke depression was documented for 13.4% and 21.6% of whites, 7.5% and 11.5% of African American, and 0% and 16.7% of patients of other race/ethnic groups. Compared with whites, African American and people of other races had a lower odds of poststroke depression (African American adjusted odds ratio = 0.52, 95% confidence interval = 0.41–0.68; other races odds ratio = 0.37, 95% confidence interval = 0.19–0.71), after adjusting for all other significant risk factors identified in the bivariate analysis (sex, hyperlipidemia, cognitive deficit, neglect). Depression was documented for one in three stroke survivors who received inpatient rehabilitation and highest among whites especially for prestroke depression. Addressing depression in rehabilitation care needs to consider individual patient characteristics and prestroke health status.

Characterizing the fault space of a cipher to filter out a set of faults potentially exploitable for fault attacks (FA), is a problem with immense practical value. A quantitative knowledge of the exploitable fault space is desirable in several applications, like security evaluation, cipher construction and implementation, design, and testing of countermeasures etc. In this work, we investigate this problem in the context of block ciphers. The formidable size of the fault space of a block cipher mandates the use of an automation strategy to solve this problem, which should be able to characterize each individual fault instance quickly. On the other hand, the automation strategy is expected to be applicable to most of the block cipher constructions. Existing techniques for automated fault attacks do not satisfy both of these goals simultaneously and hence are not directly applicable in the context of exploitable fault characterization. In this paper, we present a supervised machine learning (ML) assisted automated framework, which successfully addresses both of the criteria mentioned. The key idea is to extrapolate the knowledge of some existing FAs on a cipher to rapidly figure out new attack instances. Experimental validation of this idea on two state-of-the-art block ciphers – PRESENT and LED, establishes that our approach is able to provide fairly good accuracy in identifying exploitable fault instances at a reasonable cost. Utilizing this observation, we propose a statistical framework for exploitable fault space characterization, which can provide an estimate of the success rate of an attacker corresponding to a given fault model and fault location. The framework also returns test vectors leading towards successful attacks. As a potential application, the effect of different S-Boxes on the fault space of a cipher is evaluated utilizing the framework. Accepted version

Softmax is commonly used activation function in neural networks to normalize the output to probability distribution over predicted classes. Being often deployed in the output layer, it can potentially be targeted by fault injection attacks to create misclassification. In this extended abstract, we perform a preliminary fault analysis of Softmax against single bit faults.

In recent years, neural networks have been applied to various applications. To speed up the evaluation, a method using binarized network weights has been introduced, facilitating extremely efficient hardware implementation. Using electromagnetic (EM) side-channel analysis techniques, this study presents a framework of model extraction from practical binarized neural network (BNN) hardware. The target BNN hardware is generated and synthesized using open-source and commercial high-level synthesis tools GUINNESS and Xilinx SDSoC, respectively. With the hardware implemented on an up-to-date FPGA chip, we demonstrate how the layers can be identified from a single EM trace measured during the network evaluation, and we also demonstrate how an attacker may use side-channel attacks to recover secret weights used in the network.

Commercial machine learning accelerators like Intel neural Compute Stick 2 (NCS2) enable efficient inference on otherwise low resource edge devices. However, these accelerators are also ex- posed to new threats leveraging physical access. In this paper, we present the first results demonstrating practical electromagnetic side-channel attack on NCS2, allowing secret weight recovery from executed models National Research Foundation (NRF) This research is supported by the National Research Foundation, Singapore, under its National Cybersecurity Research & Development Programme / Cyber-Hardware Forensic & Assurance Evaluation R&D Programme (Award: NRF2018NCR-NCR009-0001)

Edge deep learning accelerators are optimised hard ware to enable efficient inference on the edge. The models deployed on these accelerators are often proprietary and thus sensitive for commercial and privacy reasons. In this paper, we demonstrate practical vulnerability of deployed deep learning models to timing side-channel attacks. By measuring the execution time of the inference, the adversary can determine and reconstruct the model from a known family of well known deep learning model and then use available techniques to recover remaining hyperparameters. The vulnerability is validated on Intel Compute Stick 2 for VGG and ResNet family of models. Moreover, the presented attack is quite devastating as it can be performed in a cross-device setting, where adversary profiles constructed on a legally own device can be used to exploit the victim device with a single query and still can achieve near perfect success rate. National Research Foundation (NRF) Accepted version This research is supported in parts by the National Research Foundation, Singapore, under its National Cybersecurity Research & Development Programme / Cyber-Hardware Forensic & Assurance Evaluation R&D Programme (Award: NRF2018NCR-NCR009-0001).

Deep learning is becoming a basis of decision making systems in many application domains, such as autonomous vehicles, health systems, etc., where the risk of misclassification can lead to serious consequences. It is necessary to know to which extent are Deep Neural Networks (DNNs) robust against various types of adversarial conditions. In this paper, we experimentally evaluate DNNs implemented in embedded device by using laser fault injection, a physical attack technique that is mostly used in security and reliability communities to test robustness of various systems. We show practical results on four activation functions, ReLu, softmax, sigmoid, and tanh. Our results point out the misclassification possibilities for DNNs achieved by injecting faults into the hidden layers of the network. We evaluate DNNs by using several different attack strategies to show which are the most efficient in terms of misclassification success rates. Outcomes of this work should be taken into account when deploying devices running DNNs in environments where malicious attacker could tamper with the environmental parameters that would bring the device into unstable conditions. resulting into faults.

Michelle, Siregar RAN, Sanjaya A, Jap L, Pinontoan R. 2020. Methylene blue decolorizing bacteria isolated from water sewage in Yogyakarta, Indonesia. Biodiversitas 21: 1136-1141. The textile industry contributes to water pollution issues all over the world. One of the most commonly applied cationic dye in the textile industry is methylene blue. This study aimed to isolate bacteria with the potential to decolorize methylene blue from dye contaminated sewage water located in Kulon Progo District, Yogyakarta, where several textile industries within the proximity, are located. Characterizations of bacterial candidates were done morphologically and biochemically. Molecular identification was conducted by 16S rRNA sequencing. The ability of isolates to decolorize methylene blue was observed by the reduction of methylene blue’s maximum absorption at the wavelength of 665 nm. The results showed that isolates were identified as Comamonas aquatica and Ralstonia mannitolilytica. C. aquatica PMB-1 and R. mannitolilytica PMB-2 isolates were able to decolorize methylene blue with decolorization percentage of 67.9% and 60.3%, respectively when incubated for 96 hours at 37°C. These findings present information on the capability of the genus Ralstonia and Comamonas to decolorize methylene blue cationic dye.

Machine learning algorithms have been widely applied to solve various type of problems and applications. Among those, decision tree based algorithms have been considered for small Internet-of-Things (IoT) implementation, due to their simplicity. It has been shown in a recent publication, that Bonsai, a small tree-based algorithm, can be successfully fitted in a small 8-bit microcontroller. However, the security of machine learning algorithm has also been a major concern, especially with the threat of secret parameter recovery which could lead to breach of privacy. With machine learning taking over a significant proportion of industrial tasks, the security issue has become a matter of concern. Recently, secret parameter recovery for neural network based algorithm using physical side-channel leakage has been proposed. In the paper, we investigate the security of widely used decision tree algorithms running on ARM Cortex M3 platform against electromagnetic (EM) side-channel attacks. We show that by focusing on each building block function or component, one could perform divide-and-conquer approach to recover the secret parameters. To demonstrate the attack, we first report the recovery of secret parameters of Bonsai, such as, sparse projection parameters, branching function and node predictors. After the recovery of these parameters, the attacker can then reconstruct the whole architecture. This work was performed in the Cooperative Research Project of the Research Institute of Electrical Communication, Tohoku University with Nanyang Technological University. This research was also supported in part by JST CREST Grant No. JPMJCR19K5, Japan.

Profiled side-channel attacks represent a practical threat to digital devices, thereby having the potential to disrupt the foundation of e-commerce, the Internet of Things (IoT), and smart cities. In the profiled side-channel attack, the adversary gains knowledge about the target device by getting access to a cloned device. Though these two devices are different in real- world scenarios, yet, unfortunately, a large part of research works simplifies the setting by using only a single device for both profiling and attacking. There, the portability issue is conveniently ignored to ease the experimental procedure. In parallel to the above developments, machine learning techniques are used in recent literature, demonstrating excellent performance in profiled side-channel attacks. Again, unfortunately, the portability is neglected. In this paper, we consider realistic side-channel scenarios and commonly used machine learning techniques to evaluate the influence of portability on the efficacy of an attack. Our experimental results show that portability plays an important role and should not be disregarded as it contributes to a significant overestimate of the attack efficiency, which can easily be an order of magnitude size. After establishing the importance of portability, we propose a new model called the Multiple Device Model (MDM) that formally incorporates the device to device variation during a profiled side-channel attack. We show through experimental studies how machine learning and MDM significantly enhance the capacity for practical side-channel attacks. More precisely, we demonstrate how MDM can improve the performance of an attack by order of magnitude, completely negating the influence of portability. National Research Foundation (NRF) Published version This research is partly supported by the Singapore National Research Foundation under its National Cybersecurity R&D Grant (“Cyber-Hardware Forensics & Assurance Evaluation R&D Programme” grant NRF2018–NCR–NCR009–0001)

Fault injection-based cryptanalysis is one of the most powerful practical threats to modern cryptographic primitives. Popular countermeasures to such fault-based attacks generally use some form of redundant computation to detect and react/correct the injected faults. However, such countermeasures are shown to be vulnerable to selective fault injections. In this article, we aim to develop a cryptographic primitive that is fault tolerant by its construction and does not require to compute the same value multiple times. We utilize the effectiveness of Neural Networks (NNs), which show “some degree” of robustness by functioning correctly even after the occurrence of faults in any of its parameters. We also propose a novel strategy that enhances the fault tolerance of the implementation to “high degree” (close to 100%) by incorporating selective constraints in the NN parameters during the training phase. We evaluated the performance of revised NN considering both software and FPGA implementations for standard cryptographic primitives like 8×8 AES SBox and 4×4 PRESENT SBox. The results show that the fault tolerance of such implementations can be significantly increased with the proposed methodology. Such NN-based cryptographic primitives will provide inherent resistance against fault injections without requiring any redundancy countermeasures.

Side-channel analysis has seen rapid adoption of deep learning techniques over the past years. While many paper focus on designing efficient architectures, some works have proposed techniques to boost the efficiency of existing architectures. These include methods like data augmentation, oversampling, regularization etc. In this paper, we compare data augmentation and oversampling (particularly SMOTE and its variants) on public traces of two side-channel protected AES. The techniques are compared in both balanced and imbalanced classes setting, and we show that adopting SMOTE variants can boost the attack efficiency in general. Further, we report a successful key recovery on ASCAD(desync=100) with 180 traces, a 50% improvement over current state of the art. Cyber Security Agency We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPU used for this research. The authors acknowledge the support from the ‘National Integrated Centre of Evaluation’ (NICE); a facility of Cyber Security Agency, Singapore (CSA).

Background Fetal growth restriction, i.e. the restriction of genetically predetermined growth potential due to placental dysfunction, is a major cause of neonatal morbidity and mortality. The consequences of inadequate fetal growth can be life-long, but the risks can be reduced substantially if the condition is identified prenatally. Currently, screening strategies are based on ultrasound detection of a small-for-gestational age fetus and do not take into account the underlying vascular pathology in the placenta. Measurement of maternal circulating angiogenic biomarkers placental growth factor, sFlt-1 (soluble FMS-like tyrosine kinase-1) are increasingly used in studies on fetal growth restriction as they reflect the pathophysiological process in the placenta. However, interpretation of the role of angiogenic biomarkers in prediction of fetal growth restriction is hampered by the varying design, population, timing, assay technique and cut-off values used in these studies. Methods We conducted a systematic-review in PubMed (MEDLINE), EMBASE (Ovid) and Cochrane to explore the predictive performance of maternal concentrations of placental growth factor, sFlt-1 and their ratio for fetal growth restriction and small-for-gestational age, at different gestational ages, and describe the longitudinal changes in biomarker concentrations and optimal discriminatory cut-off values. Results We included 26 studies with 2514 cases with small-for-gestational age, 27 cases of fetal growth restriction, 582 cases mixed small-for-gestational age/fetal growth restriction and 29,374 reference. The largest mean differences for the two biomarkers and their ratio were found after 26 weeks of gestational age and not in the first trimester. The ROC-AUC varied between 0.60 and 0.89 with sensitivity and specificity matching the different cut-off values or a preset false-positive rate of 10%. Conclusions Most of the studies did not make a distinction between small-for-gestational age and fetal growth restriction, and therefore the small-for-gestational age group consists of fetuses with growth restriction and fetuses that are constitutionally normal. The biomarkers can be a valuable screening tool for small-for-gestational age pregnancies, but unfortunately, there is not yet a clear cut-off value to use for screening. More research is needed to see if these biomarkers are sufficiently able to differentiate growth restriction on their own and how these biomarkers in combination with other relevant clinical and ultrasound parameters can be used in clinical routine diagnostics.

International audience; Machine learning techniques have significantly changed our lives. They helped improving our everyday routines, but they also demonstrated to be an extremely helpful tool for more advanced and complex applications. However, the implications of hardware security problems under a massive diffusion of machine learning techniques are still to be completely understood. This paper first highlights novel applications of machine learning for hardware security, such as evaluation of post quantum cryptography hardware and extraction of physically unclonable functions from neural networks. Later, practical model extraction attack based on electromagnetic side- channel measurements are demonstrated followed by a discussion of strategies to protect proprietary models by watermarking them.

INTRODUCTION: During systematic reviews, data abstraction is labor- and time-intensive and error-prone. Existing data abstraction systems do not track specific locations and contexts of abstracted information. To address this limitation, we developed a software application, the Data Abstraction Assistant (DAA), and surveyed early users about their experience using DAA. FEATURES OF DAA: We designed DAA to encompass three essential features: (1) a platform for indicating the source of abstracted information; (2) compatibility with a variety of data abstraction systems; and (3) user-friendliness. HOW DAA FUNCTIONS: DAA: (1) converts source documents from PDF to HTML format (to enable tracking of source of abstracted information); (2) transmits the HTML to the data abstraction system; and (3) displays the HTML in an area adjacent to the data abstraction form in the data abstraction system. The data abstractor can mark locations on the HTML that DAA associates with items on the data abstraction form. EXPERIENCES OF EARLY USERS OF DAA: When we surveyed 52 early users of DAA, 83% reported that using DAA was either very or somewhat easy; 71% are very or somewhat likely to use DAA in the future; and 87% are very or somewhat likely to recommend that others use DAA in the future. DISCUSSION: DAA, a user-friendly software for linking abstracted data with their exact source, is likely to be a very useful tool in the toolbox of systematic reviewers. DAA facilitates verification of abstracted data and provides an audit trail that is crucial for reproducible research.systematic reviews; data abstraction; software; data tracking; data exchange

Modern and future substations are aimed to be more interconnected, leveraging communication standards like IEC 61850-9-2, and associated abstract data models and communication services like generic object oriented substation event, manufacturing message specification, and sampled measured value. Such interconnection would enable fast and secure data transfer, sharing of the analytics information for various purposes like wide area monitoring, faster outage recovery, blackout prevention, distributed state estimation, etc. This would require strong focus on communication security, both at system level as well as at embedded device level. Although communication level security is dealt in IEC 62351, implementation attack on the embedded system is not considered. Since the embedded system makes the core of the smart grid, in this paper, we take a deeper look into impact of implementation attacks on substation security. An overview of potential exploits is first provided. This is followed by a case study, where implementation attacks like malicious fault injection attacks and hardware Trojan are used to compromise a substation level intelligent electronic device. The studied scenario extends implementation attacks beyond its usual exploit of confidentiality to affect power grid integrity and availability.

Introduction: Myelofibrosis (MF) is the most severe myeloproliferative neoplasia (MPN), with a very variable survival, which can be beyond 20 years to less than two years. The only curative treatment is allogeneic bone marrow transplantation (ABMT), but this is restricted to patients of less advanced age and is associated with significant mortality and disease relapse. Adequate prognostic characterization is essential for therapeutic decision-making, and modern prognostic indexes using molecular and cytogenetic data have been replacing the traditional indexes. Materials and methods: Clinical and laboratory data were evaluated, bone marrow and peripheral blood karyotype were studied, and BCR-ABL1, JAK2, CALR, MPL, ASXL1, SRSF2, EZH2, IDH1, IDH2, U2AF1 mutations were investigated. The classic IPSS, DIPSS, DIPSS plus, and the most recent MIPSS70, MIPSS70 plus, MIPSS 70 plus 2.0, GIPSS, and MYSEC-PM indexes were applied to a population of 230 patients from 8 Brazilian institutions with primary myelofibrosis (MFP) and secondary myelofibrosis polycythemia vera (PV-MF) and essential thrombocythemia (TE-MF) and compared among themselves. Results and conclusions: The median-age was 65 years-old, the overall survival was 8.1 years (CI 95%, 7,2 – 10,7), with a 5 years estimated survival of 61% (53 – 70). Comparing the prognostic indexes with each other identified a greater accuracy of the prognostic indexes that use mutational and cytogenetic data on the classic prognostic indexes. In the univariate analysis, advanced age, male gender, spleen size, hemoglobin, white blood cell count, peripheral blood blast percentage, SRSF2 mutation, triple-negative patients, IPSS, DIPSS and DIPSS plus stratification, MIPSS70 high risk and MIPSS70 plus high and very high risk showed impact in survival. In the multivariate analysis, triple-negative for JAK2 V617F, CARL, and MPL (p = 0,002) and stratification by IPSS (p < 0,0001) maintained significance as risk factors for overall survival. The present study is the most extensive Brazilian cohort study of patients with MF evaluating these variables.

Driven by the low transaction costs and interactive nature of the internet, customer participation in the price-setting process has increased. These changes were first brought about by the rise of online auctions in the early 2000s, followed by the emergence of newer participative mechanisms. Today, platforms such as eBay have popularized online auctions on a global scale, Priceline has made headlines with its name-your-own-price (NYOP) business model, and Humble Bundle has enabled independent musicians and game developers to market their works through pay-what-you-want (PWYW) pricing. Advertising exchanges conduct several hundred million individual auctions per day to sell online advertising slots. These are just a few examples of participative pricing in transactions among consumers or businesses. In parallel, academic research on participative pricing has blossomed in recent years, with an overarching concern over the profitability and other marketing implications these mechanisms have on sellers and buyers. The present paper contributes to this literature in three ways. First, we propose a definition of participative pricing mechanisms, as well as a useful taxonomy. Second, we discuss the current understanding by synthesizing conceptual and empirical academic literature. Third, we outline promising research questions with a key focus on the related behavioral aspects of buyers and sellers.