Miracle-dz/UniFAR-FTUs huggingface dataset:
Implementing Trojan-Resilient Hardware from (Mostly) Untrusted Components Designed by Colluding Manufacturers

At CCS 2016, Dziembowski et al. proved the security of a generic compiler able to transform any circuit into a Trojan-resilient one based on a (necessary) number of trusted gates. Informally, it exploits techniques from the Multi-Party Computation (MPC) literature in order to exponentially reduce the probability of a successful Trojan attack. As a result, its concrete relevance depends on ( i ) the possibility to reach good performances with affordable hardware, and ( ii ) the actual number of trusted gates the solution requires. In this paper, we assess the practicality of the CCS 2016 Trojan-resilient compiler based on a block cipher case study, and optimize its performances in different directions. From the algorithmic viewpoint, we use a recent MPC protocol by Araki et al. (CCS 2016) in order to increase the throughput of our implementations, and we investigate various block ciphers and S-box representations to reduce their communication complexity. From a design viewpoint, we develop an architecture that balances the computation and communication cost of our Trojan-resilient circuits. From an implementation viewpoint, we describe a prototype hardware combining several commercial FPGAs on a dedicated printed circuit board. Thanks to these advances, we exhibit realistic performances for a Trojan-resilient circuit purposed for high-security applications, and confirm that the amount of trusted gates required by the CCS 2016 compiler is well minimized.
Economic evaluation of a healthy lifestyle intervention for chronic low back pain: a randomised controlled trial

We performed an economic evaluation of a healthy lifestyle intervention targeting weight loss, physical activity and diet for patients with chronic low back pain, who are overweight or obese. Eligible patients with chronic low back pain (n=160) were randomised to an intervention or usual care control group. The intervention included brief advice, a clinical consultation and referral to a 6-month telephone-based healthy lifestyle coaching service. The primary outcome was quality-adjusted life years (QALYs). Secondary outcomes were pain intensity, disability, weight, and body mass index. Costs included intervention costs, healthcare utilisation costs and work absenteeism costs. An economic analysis was performed from the societal perspective. Mean total costs were lower in the intervention group than the control group (-$614; 95%CI: -3133 to 255). The intervention group had significantly lower healthcare costs (-$292; 95%CI: -872 to -33), medication costs (-$30; 95%CI: -65 to -4) and absenteeism costs (-$1000; 95%CI: -3573 to -210). For all outcomes, the intervention was on average less expensive and more effective than usual care, and the probability of the intervention being cost-effective compared to usual care was relatively high (i.e. 0.81) at a willingness-to-pay of $0/unit of effect. However, the probability of cost-effectiveness was not as favourable among sensitivity analyses. The healthy lifestyle intervention seems to be cost-effective from the societal perspective. However, variability in the sensitivity analyses indicates caution is needed when interpreting these findings.
Hawkeye: Towards a Desired Directed Grey-box Fuzzer

Grey-box fuzzing is a practically effective approach to test real-world programs. However, most existing grey-box fuzzers lack directedness, i.e. the capability of executing towards user-specified target sites in the program. To emphasize existing challenges in directed fuzzing, we propose Hawkeye to feature four desired properties of directed grey-box fuzzers. Owing to a novel static analysis on the program under test and the target sites, Hawkeye precisely collects the information such as the call graph, function and basic block level distances to the targets. During fuzzing, Hawkeye evaluates exercised seeds based on both static information and the execution traces to generate the dynamic metrics, which are then used for seed prioritization, power scheduling and adaptive mutating. These strategies help Hawkeye to achieve better directedness and gravitate towards the target sites. We implemented Hawkeye as a fuzzing framework and evaluated it on various real-world programs under different scenarios. The experimental results showed that Hawkeye can reach the target sites and reproduce the crashes much faster than state-of-the-art grey-box fuzzers such as AFL and AFLGo. Specially, Hawkeye can reduce the time to exposure for certain vulnerabilities from about 3.5 hours to 0.5 hour. By now, Hawkeye has detected more than 41 previously unknown crashes in projects such as Oniguruma, MJS with the target sites provided by vulnerability prediction tools; all these crashes are confirmed and 15 of them have been assigned CVE IDs.
From Rankings to Ratings: Rank Scoring via Active Learning

In this paper we present RaScAL, an active learning approach to predicting real-valued scores for items given access to an oracle and knowledge of the overall item-ranking. In an experiment on six different datasets, we find that RaScAL consistently outperforms the stateof-the-art. The RaScAL algorithm represents one step within a proposed overall system of preference elicitations of scores via pairwise comparisons.
The practice of support personnel, supervised by physiotherapists, in Norwegian reablement services

OBJECTIVES Reablement is a relatively new service targeting people with or at risk of functional decline. The approach is team based, and physiotherapists (PTs), occupational therapists, and nurses have the responsibility to guide and supervise the support personnel, referred to as home trainers (HTs). The aim of this study was to explore how the HTs follow up instructions and supervision by PTs in reablement. METHODS This qualitative study included video recordings of practice and individual interviews in seven Norwegian reablement teams. The analysis involved a triangulation of all data. RESULTS The results identified that HTs had the main responsibility to carry out interventions in reablement and were also expected to report back to the therapists if they recognized further need for assessment or adjustments. The content of the practices varied considerably along a continuum from rigidly standardized practices to individually tailored approaches emphasizing quality of movement. This paper presents analyses of two examples representing the two widely different approaches. CONCLUSION Practitioners and health authorities should be aware of the broad variation in reablement services in Norwegian municipalities. The results indicate that a standardized approach may be more efficient in the short term, targeting a large population, whereas a tailored approach, valuing quality of movement, is essential to provide high-quality movement training for users with complex rehabilitation needs. The target groups receiving the different reablement methods should be clearly identified.
Situational Access Control in the Internet of Things

Access control in the Internet of Things (IoT) often depends on a situation --- for example, "the user is at home'' --- that can only be tracked using multiple devices. In contrast to the (well-studied) smartphone frameworks, enforcement of situational constraints in the IoT poses new challenges because access control is fundamentally decentralized. It takes place in multiple independent frameworks, subjects are often external to the enforcement system, and situation tracking requires cross-framework interaction and permissioning. Existing IoT frameworks entangle access-control enforcement and situation tracking. This results in overprivileged, redundant, inconsistent, and inflexible implementations. We design and implement a new approach to IoT access control. Our key innovation is to introduce "environmental situation oracles'' (ESOs) as first-class objects in the IoT ecosystem. An ESO encapsulates the implementation of how a situation is sensed, inferred, or actuated. IoT access-control frameworks can use ESOs to enforce situational constraints, but ESOs and frameworks remain oblivious to each other's implementation details. A single ESO can be used by multiple access-control frameworks across the ecosystem. This reduces inefficiency, supports consistent enforcement of common policies, and --- because ESOs encapsulate sensitive device-access rights --- reduces overprivileging. ESOs can be deployed at any layer of the IoT software stack where access control is applied. We implemented prototype ESOs for the IoT resource layer, based on the IoTivity framework, and for the IoT Web services, based on the Passport middleware.
Adaptive Extreme Learning Machine for Recurrent Beta-basis Function Neural Network Training

Beta Basis Function Neural Network (BBFNN) is a special kind of kernel basis neural networks. It is a feedforward network typified by the use of beta function as a hidden activation function. Beta is a flexible transfer function representing richer forms than the common existing functions. As in every network, the architecture setting as well as the learning method are two main gauntlets faced by BBFNN. In this paper, new architecture and training algorithm are proposed for the BBFNN. An Extreme Learning Machine (ELM) is used as a training approach of BBFNN with the aim of quickening the training process. The peculiarity of ELM is permitting a certain decrement of the computing time and complexity regarding the already used BBFNN learning algorithms such as backpropagation, OLS, etc. For the architectural design, a recurrent structure is added to the common BBFNN architecture in order to make it more able to deal with complex, non linear and time varying problems. Throughout this paper, the conceived recurrent ELM-trained BBFNN is tested on a number of tasks related to time series prediction, classification and regression. Experimental results show noticeable achievements of the proposed network compared to common feedforward and recurrent networks trained by ELM and using hyperbolic tangent as activation function. These achievements are in terms of accuracy and robustness against data breakdowns such as noise signals.
Improving Machine Reading Comprehension with General Reading Strategies

Reading strategies have been shown to improve comprehension levels, especially for readers lacking adequate prior knowledge. Just as the process of knowledge accumulation is time-consuming for human readers, it is resource-demanding to impart rich general domain knowledge into a deep language model via pre-training. Inspired by reading strategies identified in cognitive science, and given limited computational resources - just a pre-trained model and a fixed number of training instances - we propose three general strategies aimed to improve non-extractive machine reading comprehension (MRC): (i) BACK AND FORTH READING that considers both the original and reverse order of an input sequence, (ii) HIGHLIGHTING, which adds a trainable embedding to the text embedding of tokens that are relevant to the question and candidate answers, and (iii) SELF-ASSESSMENT that generates practice questions and candidate answers directly from the text in an unsupervised manner. By fine-tuning a pre-trained language model (Radford et al., 2018) with our proposed strategies on the largest general domain multiple-choice MRC dataset RACE, we obtain a 5.8% absolute increase in accuracy over the previous best result achieved by the same pre-trained model fine-tuned on RACE without the use of strategies. We further fine-tune the resulting model on a target MRC task, leading to an absolute improvement of 6.2% in average accuracy over previous state-of-the-art approaches on six representative non-extractive MRC datasets from different domains (i.e., ARC, OpenBookQA, MCTest, SemEval-2018 Task 11, ROCStories, and MultiRC). These results demonstrate the effectiveness of our proposed strategies and the versatility and general applicability of our fine-tuned models that incorporate these strategies. Core code is available at https://github.com/nlpdata/strategy/.
A new consensus for evaluating CDKL5/STK9‐dependent signalling mechanisms

Mutation or inactivation of CDKL5 kinase is associated with a human neurodevelopmental condition commonly referred to as CDKL5 deficiency disorder. § Two recent phosphoproteomics studies identify the first physiological substrates of mammalian CDKL5 and evaluate functional consequences of their phosphorylation and its loss in cells lacking functional CDKL5, highlighting potential roles for this kinase in regulating neuronal microtubule dynamics.
Engineering molecular imaging strategies for regenerative medicine

Abstract The reshaping of the world's aging population has created an urgent need for therapies for chronic diseases. Regenerative medicine offers a ray of hope, and its complex solutions include material, cellular, or tissue systems. We review basics of regenerative medicine/stem cells and describe how the field of molecular imaging, which is based on quantitative, noninvasive, imaging of biological events in living subjects, can be applied to regenerative medicine in order to interrogate tissues in innovative, informative, and personalized ways. We consider aspects of regenerative medicine for which molecular imaging will benefit. Next, genetic and nanoparticle‐based cell imaging strategies are discussed in detail, with modalities like magnetic resonance imaging, optical imaging (near infra‐red, bioluminescence), raman microscopy, and photoacoustic microscopy), ultrasound, computed tomography, single‐photon computed tomography, and positron emission tomography. We conclude with a discussion of “next generation” molecular imaging strategies, including imaging host tissues prior to cell/tissue transplantation.
The tyrosine phosphatase Shp‐2 confers resistance to colonic inflammation by driving goblet cell function and crypt regeneration

The Src homology‐2 domain‐containing tyrosine phosphatase 2 (SHP‐2) regulates many cellular processes, including proliferation, differentiation and survival. Polymorphisms in the gene encoding SHP‐2 are associated with an increased susceptibility to develop ulcerative colitis. We recently reported that intestinal epithelial cell (IEC)‐specific deletion of Shp‐2 in mice (Shp‐2IEC‐KO) leads to chronic colitis and colitis‐associated cancer. This suggests that SHP‐2‐dependent signaling protects the colonic epithelium against inflammation and colitis‐associated cancer development. To verify this hypothesis, we generated mice expressing the Shp‐2 E76K activated form specifically in IEC. Our results showed that sustained Shp‐2 activation in IEC increased intestine and crypt length, correlating with increased cell proliferation and migration. Crypt regeneration capacity was also markedly enhanced, as revealed by ex vivo organoid culture. Shp‐2 activation alters the secretory cell lineage, as evidenced by increased goblet cell numbers and mucus secretion. Notably, these mice also demonstrated elevated ERK signaling in IEC and exhibited resistance against both chemical‐ and Citrobacter rodentium‐induced colitis. In contrast, mice with IEC‐specific Shp‐2 deletion displayed reduced ERK signaling and rapidly developed chronic colitis. Remarkably, expression of an activated form of Braf in Shp‐2‐deficient mice restored ERK activation, goblet cell production and prevented colitis. Altogether, our results indicate that chronic activation of Shp‐2/ERK signaling in the colonic epithelium confers resistance to mucosal erosion and colitis. © 2018 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland.
Age-related impairment in Addenbrooke’s cognitive examination revised scores in patients with amyotrophic lateral sclerosis

Abstract Objective: Older age is thought to be a risk factor for cognitive impairment in amyotrophic lateral sclerosis (ALS). However, very few clinical studies have investigated this relationship using sufficient numbers of healthy controls that correspond to each generation. The purpose of this study was to determine the age-related changes of Addenbrooke’s Cognitive Examination-Revised (ACE-R) score in ALS patients by comparing healthy controls of various ages. Methods: 131 ALS patients (86 males, 45 females; mean age: 64.8 ± 10.2; mean education: 12.5 ± 2.7) and 151 age-, gender-, and education-matched healthy controls were enrolled. We applied ACE-R, which could evaluate not only global cognition but five cognitive subdomains that included orientation/attention, memory, verbal fluency, language, and visuospatial ability. Results: ALS patients had significantly lower total and subdomain scores of ACE-R than healthy controls. Multiple regression analysis suggested that age at examination and age at onset had significant influence on ACE-R scores. When we divided ALS patients and healthy controls into 4 groups according to age at examination for ALS, total and each subdomain scores were significantly lower with age, particularly in the older-middle and the oldest group (66.31 years or more) of ALS compared with healthy controls. Locally weighted scatterplot smoothing analysis supported that these reductions of ACE-R total and subdomain scores in ALS patients were more accelerated by approximately 60 years as compared with healthy controls. Conclusion: ALS patients showed accelerated age-related ACE-R score reduction beyond normal ageing processes.
Voting: You Can't Have Privacy without Individual Verifiability

Electronic voting typically aims at two main security goals: vote privacy and verifiability. These two goals are often seen as antagonistic and some national agencies even impose a hierarchy between them: first privacy, and then verifiability as an additional feature. Verifiability typically includes individual verifiability (a voter can check that her ballot is counted); universal verifiability (anyone can check that the result corresponds to the published ballots); and eligibility verifiability (only legitimate voters may vote). We show that actually, privacy implies individual verifiability. In other words, systems without individual verifiability cannot achieve privacy (under the same trust assumptions). To demonstrate the generality of our result, we show this implication in two different settings, namely cryptographic and symbolic models, for standard notions of privacy and individual verifiability. Our findings also highlight limitations in existing privacy definitions in cryptographic settings.
Integrative Analysis of Transcriptomic and Methylomic Data in Photoperiod-Dependent Regulation of Cucumber Sex Expression

The cucumber (Cucumis sativus) is characterized by its diversity and seasonal plasticity in sexual type. A long day length condition significantly decreased the cucumber female flower ratio by 17.7–52.9%, and the effect of photoperiod treatment is more significant under low temperature than under high temperature. Transcriptome analysis indicates that the photoperiod treatment preferentially significantly influenced flower development processes, particularly MADS-box genes in shoot apices. The long-day treatment resulted in predominantly transposable element (TE)- and gene-associated CHH-types of DNA methylation changes. Nevertheless, there was significant enrichment of CG- and CHG-types of DNA methylation changes nearing transcription start sites (TSSs)/transcription end sites (TESs) and gene bodies, respectively. Predominantly negative association between differentially methylated regions (DMRs) and differentially expressed genes (DEGs) were observed which implied epiregulation of DEGs. Two MADS-box genes that were significantly downregulated by long photoperiod showed significant hypermethylation in promoter regions that is essentially TE-rich. This study indicates MADS-box genes which are partially regulated by promoter methylation state may mediate photoperiod-dependent regulation of cucumber sex expression.
Multirobot Coordination With Counting Temporal Logics

In many multirobot applications, planning trajectories in a way to guarantee that the collective behavior of the robots satisfies a certain high-level specification is crucial. Motivated by this problem, we introduce counting temporal logics—formal languages that enable concise expression of multirobot task specifications over possibly infinite horizons in this article. We first introduce a general logic called counting linear temporal logic plus (cLTL+), and propose an optimization-based method that generates individual trajectories such that satisfaction of a given cLTL+ formula is guaranteed when these trajectories are synchronously executed. We then introduce a fragment of cLTL+, called counting linear temporal logic (cLTL), and show that a solution to a planning problem with cLTL constraints can be obtained more efficiently if all robots have identical dynamics. In the second part of this article, we relax the synchrony assumption and discuss how to generate trajectories that can be asynchronously executed, while preserving the satisfaction of the desired cLTL+ specification. In particular, we show that when the asynchrony between robots is bounded, the method presented in this article can be modified to generate robust trajectories. We demonstrate these ideas with an experiment and provide numerical results that showcase the scalability of the method.
Policies Modulating Trajectory Generators

We propose an architecture for learning complex controllable behaviors by having simple Policies Modulate Trajectory Generators (PMTG), a powerful combination that can provide both memory and prior knowledge to the controller. The result is a flexible architecture that is applicable to a class of problems with periodic motion for which one has an insight into the class of trajectories that might lead to a desired behavior. We illustrate the basics of our architecture using a synthetic control problem, then go on to learn speed-controlled locomotion for a quadrupedal robot by using Deep Reinforcement Learning and Evolutionary Strategies. We demonstrate that a simple linear policy, when paired with a parametric Trajectory Generator for quadrupedal gaits, can induce walking behaviors with controllable speed from 4-dimensional IMU observations alone, and can be learned in under 1000 rollouts. We also transfer these policies to a real robot and show locomotion with controllable forward velocity.
A Study on the Design of Fog Computing Architecture Using Sensor Networks

It is expected that the number of devices connecting to the Internet-of-Things (IoT) will increase geometrically in the future, with improvement of their functions. Such devices may create a huge amount of data to be processed in a limited time. Under the IoT environment, data management should play the role of an intermediate level between objects and devices that generate data and applications that access to the data for analysis and the provision of services. IoT interactively connects all communication devices and allows global access to the data generated by a device. Fog computing manages data and computation at the edge of the network near an end user and provides new types of applications and services, with low latency, high frequency bandwidth and geographical distribution. In this paper, we propose a fog computing architecture for efficiently and reliably delivering IoT data to the corresponding IoT applications while ensuring time sensitivity. Based on fog computing, the proposed architecture provides efficient power management in IoT device communication between sensors and secure management of data to be decrypted based on user attributes. The functional effectiveness and the safe data management of the method proposed are compared through experiments.