Miracle-dz/UniFAR-FTUs huggingface dataset:
Steganographer Detection based on Multiclass Dilated Residual Networks

Steganographer detection task is to identify criminal users, who attempt to conceal confidential information by steganography methods, among a large number of innocent users. The significant challenge of the task is how to collect the evidences to identify the guilty user with suspicious images, which are embedded with secret messages generating by unknown steganography and payload. Unfortunately, existing methods for steganalysis were served for the binary classification. It makes them harder to classify the images with different kinds of payloads, especially when the payloads of images in test dataset have not been provided in advance. In this paper, we propose a novel steganographer detection method based on multiclass deep neural networks. In the training stage, the networks are trained to classify the images with six types of payloads. The networks can preserve even strengthen the weak stego signals from secret messages in much larger receptive filed by virtue of residual and dilated residual learning. In the inference stage, the learnt model is used to extract the discriminative features, which can capture the difference between guilty users and innocent users. A series of empirical experimental results demonstrate that the proposed method achieves good performance in spatial and frequency domains even though the embedding payload is low. The proposed method achieves a higher level of robustness of inter-steganographic algorithms and can provide a possible solution to address the payload mismatch problem
Static serializability analysis for causal consistency

Many distributed databases provide only weak consistency guarantees to reduce synchronization overhead and remain available under network partitions. However, this leads to behaviors not possible under stronger guarantees. Such behaviors can easily defy programmer intuition and lead to errors that are notoriously hard to detect. In this paper, we propose a static analysis for detecting non-serializable behaviors of applications running on top of causally-consistent databases. Our technique is based on a novel, local serializability criterion and combines a generalization of graph-based techniques from the database literature with another, complementary analysis technique that encodes our serializability criterion into first-order logic formulas to be checked by an SMT solver. This analysis is more expensive yet more precise and produces concrete counter-examples. We implemented our methods and evaluated them on a number of applications from two different domains: cloud-backed mobile applications and clients of a distributed database. Our experiments demonstrate that our analysis is able to detect harmful serializability violations while producing only a small number of false alarms.
Effect of Polyphenols From Campomanesia adamantium on Platelet Aggregation and Inhibition of Cyclooxygenases: Molecular Docking and in Vitro Analysis

Campomanesia adamantium is a medicinal plant of the Brazilian Cerrado. Different parts of its fruits are used in popular medicine to treat gastrointestinal disorders, rheumatism, urinary tract infections and inflammations. Despite its widespread use by the local population, the mechanisms involving platelet aggregation and the inhibition of cyclooxygenase by C. adamantium are unknown. This study evaluated the chemical composition, antioxidant activities and potential benefits of the C. adamantium peel extract (CAPE) and its components in the platelet aggregation induced by arachidonic acid in platelet-rich plasma. Aspects of the pharmacological mechanism were investigated as follows: platelet viability, calcium mobilization, levels of the cyclic nucleotides cAMP and cGMP, thromboxane B2 levels, and the inhibitory effects on COX-1 and COX-2 were studied in vitro and using molecular docking in the catalytic domain of these proteins. The major CAPE constituents standing out from the chemical analysis are the flavonoids, namely those of the flavones and chalcones class. The results showed that CAPE, quercetin and myricetin significantly decreased arachidonic acid-induced platelet aggregation; the assays showed that CAPE and quercetin decreased the mobilization of calcium and thromboxane B2 levels in platelets and increased cAMP and cGMP levels. Moreover, CAPE inhibited the activity of COX-1 and COX-2, highlighting that quercetin could potentially prevent the access of arachidonic acid more to the catalytic site of COX-1 than COX-2. These results highlight CAPE’s potential as a promising therapeutic candidate for the prevention and treatment of diseases associated with platelet aggregation.
Evaluation of Register Number Abstraction for Enhanced Instruction Register Files

Instruction set randomization (ISR) is a cost-effective obfuscation technique that modifies or enhances the relationship between instructions and machine languages. An Instruction Register File (IRF), a list of frequently used instructions, can be used for ISR by providing the way of indirect access to them. This study examines the IRF that integrates a positional register, which was proposed as a supplementary unit of the IRF, for the sake of tamper resistance. According to our evaluation, with a new design for the contents of the positional register, the measure of tamper resistance was increased by 8.2% at a maximum, which corresponds to a 32.2% increase in the size of the IRF. The number of logic elements increased by the addition of the positional register was 3.5% of its baseline processor. key words: computer architecture, embedded systems, instruction register files, secure processors
Application of Magneto-Inductive Metamaterial Lenses in Parallel Imaging

Introduction The magneto-inductive (MI) lens is a near-field imaging metamaterial [1] device which was initially investigated in the microwave range [2]. A few years later, the imaging properties of this device in the MHz range were investigated and potential applications in the field of parallel MRI were also discussed [3,4]. Basically, the MI lens consists of a pair of parallel 2D arrays of resonators which are inductively coupled between them. This structure has the ability to transfer the magnetic RF field coming from a source such as a MRI coil to an imaging plane which is placed on the other side of the device, similar to a lens in optics. Recently, a single loop setup with a MI lens has shown a significant improvement of SNR compared to a standard single loop coil without lens [5]. The present work investigates the capabilities of MI lenses in array setups for parallel imaging.
Synergistic effect of acidity and extraframework position in faujasite on renewable p-xylene production

p-Xylene is a commodity chemical used for the manufacture of plastic bottles and textiles. For the biomass-based route from 2,5-dimethylfuran (DMF) and ethylene, the properties of the catalyst such as acidity effect, product selectivity and catalyst activity play an important role. To determine the effect of acidity and extraframework position in faujasite zeolite on p-xylene selectivity, type Y (Si/Al = 40 and Si/Al = 2.55) and X (Si/Al = 1.25) zeolites containing the extraframework Lewis acids Na+, K+, Li+, Ag+ and Cu+, and a Brønsted acid-containing zeolite, HY (Si/Al = 40), were prepared and tested for p-xylene production under solvent-free conditions and at low conversions (less than 35%). Here it is reported that NaX zeolite catalyses DMF and ethylene conversion to p-xylene with 91% selectivity at 30% conversion, which is better than the 25% p-xylene selectivity obtained when using HY at similar conversion. ANOVA was used to show that there is a synergistic effect between acidity and extraframework position on the rate of p-xylene production. At 7% DMF conversion, Lewis acids were more selective than the Brønsted acid tested (50 versus 30% p-xylene selectivity). p-Xylene selectivity is optimal when using Lewis acids with moderate acidity and extraframework positions located in the faujasite supercage (sites II and III).
Radial Multi-Coil Imaging

INTRODUCTION: The multi-coil (MC) technique enables the generation of a multitude of magnetic field shapes relevant to MR applications. Magnetic fields are synthesized with the MC approach by a scaled superposition of generic (not necessarily orthogonal) field shapes from individual basis coils and the method stands out by its ability to flexibly trade volume-of-interest, field accuracy and generation efficiency for the experiment at hand [1]. After the application of MC modeling to the homogenization of magnetic fields in the mouse and the human brain [2,3], here we demonstrate the first direct MC imaging application in which magnetic fields are generated by MC modeling and dynamically applied in a regular MR sequence scheme.
[Journal First] On the Use of Hidden Markov Model to Predict the Time to Fix Bugs

A significant amount of time is spent by software developers in investigating bug reports. It is useful to indicate when a bug report will be closed, since it would help software teams to prioritise their work. Several studies have been conducted to address this problem in the past decade. Most of these studies have used the frequency of occurrence of certain developer activities as input attributes in building their prediction models. However, these approaches tend to ignore the temporal nature of the occurrence of these activities. In this paper, a novel approach using Hidden Markov models (HMMs) and temporal sequences of developer activities is proposed. The approach is empirically demonstrated in a case study using eight years of bug reports collected from the Firefox project.
Debugging with Intelligence via Probabilistic Inference

We aim to debug a single failing execution without the assistance from other passing/failing runs. In our context, debugging is a process with substantial uncertainty – lots of decisions have to be made such as what variables shall be inspected first. To deal with such uncertainty, we propose to equip machines with human-like intelligence. Specifically, we develop a highly automated debugging technique that aims to couple human-like reasoning (e.g., dealing with uncertainty and fusing knowledge) with program semantics based analysis, to achieve benefits from the two and mitigate their limitations. We model debugging as a probabilistic inference problem, in which the likelihood of each executed statement instance and variable being correct/faulty is modeled by a random variable. Human knowledge, human-like reasoning rules and program semantics are modeled as conditional probability distributions, also called probabilistic constraints. Solving these constraints identifies the most likely faulty statements. Our results show that the technique is highly effective. It can precisely identify root causes for a set of real-world bugs in a very small number of interactions with developers, much smaller than a recent proposal that does not encode human intelligence. Our user study also confirms that it substantially improves human productivity.
Kernel sparse representation for MRI image analysis in automatic brain tumor segmentation

The segmentation of brain tumor plays an important role in diagnosis, treatment planning, and surgical simulation. The precise segmentation of brain tumor can help clinicians obtain its location, size, and shape information. We propose a fully automatic brain tumor segmentation method based on kernel sparse coding. It is validated with 3D multiple-modality magnetic resonance imaging (MRI). In this method, MRI images are pre-processed first to reduce the noise, and then kernel dictionary learning is used to extract the nonlinear features to construct five adaptive dictionaries for healthy tissues, necrosis, edema, non-enhancing tumor, and enhancing tumor tissues. Sparse coding is performed on the feature vectors extracted from the original MRI images, which are a patch of m×m×m around the voxel. A kernel-clustering algorithm based on dictionary learning is developed to code the voxels. In the end, morphological filtering is used to fill in the area among multiple connected components to improve the segmentation quality. To assess the segmentation performance, the segmentation results are uploaded to the online evaluation system where the evaluation metrics dice score, positive predictive value (PPV), sensitivity, and kappa are used. The results demonstrate that the proposed method has good performance on the complete tumor region (dice: 0.83; PPV: 0.84; sensitivity: 0.82), while slightly worse performance on the tumor core (dice: 0.69; PPV: 0.76; sensitivity: 0.80) and enhancing tumor (dice: 0.58; PPV: 0.60; sensitivity: 0.65). It is competitive to the other groups in the brain tumor segmentation challenge. Therefore, it is a potential method in differentiation of healthy and pathological tissues.
The Development Designer Perspective

When creating a decision support tool to assist the selection of a software interface design it is crucial for the user experience team to understand how members of development make design decisions. How do developers think about design and what terminologies do they apply to the design process? Fifteen in-depth interviews were conducted with developers and product managers responsible for interface design decisions. Results indicated that members of development focused mainly on making new features consistent with their current applications and not innovating new interfaces. Development's design decision making process relied heavily on input from peers and senior staff. Development's terminology for categories that are factors in design (e.g., user type, user task, layout structure and data structure) differed from the version conceived by user experience. The synthesis of the two terminologies has created a basis for building a development decision support tool.
Behavioural elements and sensory cues involved in sexual isolation between Drosophila melanogaster strains

Sensory cues exchanged during courtship are crucial for mate choice: if they show intraspecific divergence, this may cause or reinforce sexual isolation between strains, ultimately leading to speciation. There is a strong asymmetric sexual isolation between Drosophila melanogaster females from Zimbabwe (Z) and males from all other populations (M). While M and Z flies of both sexes show different cuticular pheromones, this variation is only partly responsible for the intraspecific isolation effect. Male acoustic signals are also partly involved in sexual isolation. We examined strain-specific courtship behaviour sequences to determine which body parts and sensory appendages may be involved in sexual isolation. Using two strains representative of the Z- and M-types, we manipulated sensory cues and the social context; we then measured the consequence of these manipulations on courtship and copulation. Our data suggest that Z females mated best with males whose sensory characteristics matched those of Z males in both quantity and quality. M females were less choosy and much less influenced by the sensory and social contexts. Differences in emission and reception of sensory signals seen between Z and M flies may lead to the concerted evolution of multiple sensory channel, thereby shaping a population-specific mate recognition system.
DroidStar: Callback Typestates for Android Classes

Event-driven programming frameworks, such as Android, are based on components with asynchronous interfaces. The protocols for interacting with these components can often be described by finite-state machines we dub *callback typestates. Callback typestates are akin to classical typestates, with the difference that their outputs (callbacks) are produced asynchronously. While useful, these specifications are not commonly available, because writing them is difficult and error-prone. Our goal is to make the task of producing callback typestates significantly easier. We present a callback typestate assistant tool, DroidStar, that requires only limited user interaction to produce a callback typestate. Our approach is based on an active learning algorithm, L*. We improved the scalability of equivalence queries (a key component of L*), thus making active learning tractable on the Android system. We use DroidStar to learn callback typestates for Android classes both for cases where one is already provided by the documentation, and for cases where the documentation is unclear. The results show that DroidStar learns callback typestates accurately and efficiently. Moreover, in several cases, the synthesized callback typestates uncovered surprising and undocumented behaviors.
A Robust Algorithm Framework for Small DTI Samples

Introduction Diffusion tensor imaging (DTI) is a very powerful technique with a great clinical and research potential. DTI allows one to perform a noninvasive, in vivo tissue characterisation of human brain pathologies in relatively short acquisition times. The rotational invariants of DTI such as an apparent diffusion coefficient, fractional anisotropy, etc., have established themselves as valuable biomarkers in various brain disorders. The estimation methods play a key role in tensor assessment and, in turn, in the consistency of potential diagnostic outcome. In clinical measurements, DTI data sets are typically acquired with only a very few repetitions and might be strongly corrupted by artefacts. The robust statistics allow us to localize and erase the outliers caused by physiological noise [1,2]. However, the robust estimators in small samples present a rather complicated problem [3-5]. In this work, we compare the efficiency of different algorithms for small DTI samples that are especially sensitive to various artefacts such as cardiac pulsation, bulk head motion, etc. Theory and Methods We applied three methods of diffusion tensor estimation: weighted least squares (WLS) [6], RESTORE based on the M-estimator [1], and the algorithm developed by us and based on the least trimmed squares (LTS) [2] and median absolute deviation (MAD) estimators. The LTS algorithm exploits the trimmed squares of the arranged
Association Genetics in Populus Reveal the Allelic Interactions of Pto-MIR167a and Its Targets in Wood Formation

MicroRNAs (miRNAs) play crucial regulatory roles in plant growth and development by interacting with RNA molecules, including messenger RNAs (mRNAs) and long non-coding RNAs (lncRNAs); however, the genetic networks of miRNAs and their targets influencing the phenotypes of perennial trees remain to be investigated. Here, we integrated expression profiling and association analysis of underlying physiology and expression traits to dissect the allelic variations and genetic interactions of Pto-MIR167a and its targets, sponge lncRNA ARFRL, and Pto-ARF8, in 435 unrelated individuals of Populus tomentosa. Tissue-specific expression analysis in eight tissues, including stem, leaf, root, and shoot apex, revealed negative correlations between Pto-MIR167a and lncRNA ARFRL and Pto-ARF8 (r = −0.60 and −0.61, respectively, P < 0.01), and a positive correlation between sponge lncRNA ARFRL and Pto-ARF8 (r = 0.90, P < 0.01), indicating their potential regulatory roles in tree growth and wood formation. Single nucleotide polymorphism (SNP)-based association studies detected 53 significant associations (P < 0.01, Q < 0.1) representing 41 unique SNPs from the three genes and six traits, suggesting their potential roles in wood formation. Epistasis uncovered 88 pairwise interactions for 10 traits, which provided substantial evidence for genetic interactions among Pto-MIR167a, lncRNA ARFRL, and Pto-ARF8. Using gene expression-based association mapping, we also examined SNPs within the three genes that influence phenotypes by regulating the expression of Pto-ARF8. Interestingly, SNPs in the precursor region of Pto-MIR167a altered its secondary structure stability and transcription, thereby affecting the expression of its targets. In summary, we elucidated the genetic interactions between Pto-MIR167a and its targets, sponge lncRNA ARFRL, and Pto-ARF8, in tree growth and wood formation, and provide a feasible method for further investigation of multi-factor genetic networks influencing phenotypic variation in the population genetics of trees.
On oracle-type local recovery guarantees in compressed sensing

We present improved sampling complexity bounds for stable and robust sparse recovery in compressed sensing. Our unified analysis based on l1 minimization encompasses the case where (i) the measurements are block-structured samples in order to reflect the structured acquisition that is often encountered in applications; (ii) the signal has an arbitrary structured sparsity, by results depending on its support S. Within this framework and under a random sign assumption, the number of measurements needed by l1 minimization can be shown to be of the same order than the one required by an oracle least-squares estimator. Moreover, these bounds can be minimized by adapting the variable density sampling to a given prior on the signal support and to the coherence of the measurements. We illustrate both numerically and analytically that our results can be successfully applied to recover Haar wavelet coefficients that are sparse in levels from random Fourier measurements in dimension one and two, which can be of particular interest in imaging problems. Finally, a preliminary numerical investigation shows the potential of this theory for devising adaptive sampling strategies in sparse polynomial approximation.
ACM SIGSPATIAL GIS Cup 2017: range queries under Fréchet distance

The 25th ACM SIGSPATIAL GIS Conference on Advances in Geographic Information Systems was held in November 2017. In conjunction with the main conference, we organized the 6th GIS-focused algorithm competition called the ACM SIGSPATIAL GIS Cup 2017. The contest was about calculating range queries using the Fréchet distance of trajectories in mobility datasets.
A Situated Exploration of Designing for Personal Health Ecosystems through Data-enabled Design

This paper presents a design case study from industry that explores designing for personal health ecosystems. Following on from previous work on ecologies that gives a predominantly theoretical perspective, we present a more applied and design-oriented perspective. To do so, we build on our previously-developed data-enabled design approach, which utilizes contextual, behavioral and experiential data from situated design experiments as creative material. This approach comprises two steps, of which this paper presents the first (contextual) step. We introduced a small adaptable ecosystem of multiple artifacts, in four family homes over a period of eight weeks, through which we explored and further expanded on valuable ecosystem relationships. The insights gained were translated into three design opportunities that inform a future second step. We highlight and discuss practical examples from our situated explorations, and discuss how our data-enabled design approach served in designing for the complexity and versatility inherent to these ecosystems.
Internet of Environmental Things: A Human Centered Approach

With the continuous urbanization and industrial growth, the concern about deteriorating air quality is also increasing. This directly impacts the human health and sustainable development. The increase in issues like smog and other issues affecting air quality have adversely influenced the quality of life and even has affected the economic growth in a negative way. In order to move ahead with the idea of "smart" world, it is important to have systems which can assist in designing smart environment systems, smart health applications and smart navigation frameworks. The aim is to exploit the mobile sensing, IoT (Internet of Things) technology and Artificial Intelligence to come up with novel solutions to do air quality sensing and provide services to the people which will not just raise awareness related to air quality problem but also assist them in day to day living.