Miracle-dz/UniFAR-FTUs huggingface dataset:
Memories from the ergodic phase: the awkward dynamics of spherical mixed p-spin models

We revisit the long-time limit of the out of equilibrium dynamics of mean-field spherical mixed p-spin models. We consider quenches (gradient descent dynamics) starting from initial conditions thermalized at some temperature in the ergodic phase. We perform numerical integration of the dynamical mean-field equations of the model and we find an unexpected dynamical phase transition. Below an onset temperature $T_{onset}$, higher than the dynamical transition temperature $T_{MCT} $, the asymptotic energy goes below the "threshold energy" of the dominant marginal minima of the energy function and memory of the initial condition is kept. This behavior, not present in the pure spherical p-spin , resembles closely the one observed in simulations of glass forming liquids. We then investigate the nature of asymptotic dynamics, finding an aging state that relaxes towards deep marginal minima. Careful analysis however rules out simple aging solutions, leaving open a full comprehension of the memory effect in these models.
Pharmacognostical Studies on Acer campestre L. subsp. campestre

This study on Acer campestre L. subsp. campestre , used traditionally in treatments of various inflammation disorders, especially hemorrhoids and rheumatism, aimed to determine the total phenolic contents, the antioxidant and antimicrobial potentials, and the chemical composition profile of the extracts and subextracts from maple syrup and Acer campestre L. subsp. campestre leaves and twigs. The antioxidant activities of maple syrup extracts and subextracts were examined by 2,2-diphenyl-1-picryl-hydrazyl (DPPH), and the total phenolic contents of all samples were determined using the Folin-Ciocalteu reagent (FCR) method. Also, they were tested for their antimicrobial activities by microbroth dilution technique. Catechic tannin, saponins and flavonoids are determined in its leaves and twigs by preliminary qualitative phytochemical analysis. The alcoholic extracts obtained by 48-hour maceration (91.2%), ethyl acetate subextract of alcohol extracts obtained by 24-hour maceration (91.1%) and alcohol extracts obtained by 24-hour maceration (88.8%) exhibited higher radical scavenging activity than other samples, while the decoction and infusion had moderate activities. Beside of this, it was found that the syrup and its subextracts have more total phenolic contents than other extracts. The alcohol extracts obtained by 24-hour maceration and by 48-hour maceration exhibited higher activity against Candida albicans (78 μg/mL and 156 μg/mL, respectively). Only the alcohol extract obtained by 48-hour maceration and ethyl acetate subextract of syrup showed an activity against Escherichia coli , while all samples except butanol subextract of syrup have an antibacterial activity against Pseudomonas aeruginosa .
Software Engineering Data Analytics: A Framework Based on a Multi-Layered Abstraction Mechanism

SUMMARY This paper presents a concept of a domain-speciﬁc framework for software analytics by enabling querying, modeling, and integration of heterogeneous software repositories. The framework adheres to a multi-layered abstraction mechanism that consists of domain-speciﬁc operators. We showcased the potential of this approach by employing a case study.
A Spatially Variable Time Series of Sea Level Change Due to Artificial Water Impoundment

The artificial impoundment of water behind dams causes global mean sea level (GMSL) to fall as reservoirs fill but also generates a local rise in sea level due to the increased mass in the reservoir and the crustal deformation this mass induces. To estimate spatiotemporal fluctuations in sea level due to water impoundment, we use a historical data set that includes 6,329 reservoirs completed between 1900 and 2011, as well as projections of 3,565 reservoirs that are expected to be completed by 2040. The GMSL change associated with the historical data (−0.2 mm yr−1 from 1900–2011) is consistent with previous studies, but the temporal and spatial resolution allows for local studies that were not previously possible, revealing that some locations experience a sea level rise of as much as 40 mm over less than a decade. Future construction of reservoirs through ~2040 is projected to cause a GMSL fall whose rate is comparable to that of the last century (−0.3 mm yr−1) but with a geographic distribution that will be distinct from the last century, including a rise in sea level in more coastal areas. The analysis of expected construction shows that significant impoundment near coastal communities in the coming decades could enhance the flooding risk already heightened by global sea level rise.
Snape: The Dark Art of Handling Heterogeneous Enclaves

Code executing on the edge needs to run on hardware platforms that feature different memory architectures, virtualization extensions, and using a range of security features. Forcing application code to conform to a monolithic API such as POSIX, or ABI such as Linux, ties developers into large, complex platforms that make it difficult to use such hardware-specific features effectively as well as coming with their own baggage and the attendant security issues. As edge computing proliferates, handling increasingly sensitive and intimate data in our everyday lives, it becomes important for developers to be able to use all the hardware resources of their particular platform, correctly and efficiently. To this end, we propose Snape, an API and composable platform for matching applications' needs to the available hardware features in a heterogeneous environment. Unlike existing solutions, Snape provides applications with a flexible trust model and replaces untrusted host OS services with corresponding hw-assisted secured services. We report experience with our proof-of-concept implementation that enables Solo5 unikernels on Raspberry Pi 3 boards to make effective use of ARM TrustZone security technology.
Hourglass: Leveraging Transient Resources for Time-Constrained Graph Processing in the Cloud

This paper addresses the key problems that emerge when one attempts to use transient resources to reduce the cost of running time-constrained jobs in the cloud. Previous works fail to address these problems and are either not able to offer significant savings or miss termination deadlines. First, the fact that transient resources can be evicted, requiring the job to be re-started (even if not from scratch) may lead provisioning policies to fall-back to expensive on-demand configurations more often than desirable, or even to miss deadlines. Second, when a job is restarted, the new configuration can be different from the previous, which might make eviction recovery costly, e.g., transferring the state of graph data between the old and new configurations. We present HOURGLASS, a system that addresses these issues by combining two novel techniques: a slack-aware provisioning strategy that selects configurations considering the remaining time before the job's termination deadline, and a fast reload mechanism to quickly recover from evictions. By switching to an on-demand configuration when (but only if) the target deadline is at risk of not being met, we are able to obtain significant cost savings while always meeting the deadlines. Our results show that, unlike previous work, HOURGLASS is able to significantly reduce the operating costs in the order of 60-70% while guaranteeing that deadlines are met.
BootKeeper: Validating Software Integrity Properties on Boot Firmware Images

Boot firmware, like UEFI-compliant firmware, has been the target of numerous attacks, giving the attacker control over the entire system while being undetected. The measured boot mechanism of a computer platform ensures its integrity by using cryptographic measurements to detect such attacks. This is typically performed by relying on a Trusted Platform Module (TPM). Recent work, however, shows that vendors do not respect the specifications that have been devised to ensure the integrity of the firmware's loading process. As a result, attackers may bypass such measurement mechanisms and successfully load a modified firmware image while remaining unnoticed. In this paper we introduce BootKeeper, a static analysis approach verifying a set of key security properties on boot firmware images before deployment, to ensure the integrity of the measured boot process. We evaluate BootKeeper against several attacks on common boot firmware implementations and demonstrate its applicability.
Berkooliah: Utilizing Social Media to Encourage Youths in Pursuing Higher Education

In Indonesia, there is a high number of people that stopped looking for higher opportunities in education after finishing the compulsory program set by the government (12 years). This phenomenon opposes what the Sustainable Development Goals (SDGs) designed by United Nations regarding what the UN wished for the world's universal development goals to be achieved in 2030. Across 17 domains of goals, the fourth goal is to ensure inclusive and equitable quality education and promote lifelong opportunities for all, and in the third indicator we found that we should ensure equal access to affordable education including university. It is expected that access to higher education leads to better opportunities landing jobs that brings economic stability to family, which in turns leads to better well-being for the next generation. As we know, youth population today would be the leader of Indonesia in the upcoming decade, and there is a shift to information access from traditional media to social media. We believe by utilizing social media to give complete information about university, we can encourage youths to pursue higher education which in turn will benefits for them individually and society as a whole. According to this issue, we decided to make "Berkooliah", an Instagram account (Instagram.com/berkooliah) as the tool of the intervention program. After five months of running the program from April to September 2018, we can conclude that this program goes well as the initial plan since the data shows a high enthusiasm and positive feedbacks from the online community.
Recognition of Collocation Frames from Sentences

Collocation is a ubiquitous phenomenon in languages and accurate collocation recognition and extraction is of great significance to many natural language processing tasks. Collocations can be differentiated from simple bigram collocations to collocation frames (referring to distant multi-gram collocations). So far little focus is put on collocation frames. Oriented to translation and parsing, this study aims to recognize and extract the longest possible collocation frames from given sentences. We first extract bigram collocations with distributional semantics based method by introducing collocation patterns and integrating some state-of-the-art association measures. Based on bigram collocations extracted by the proposed method, we get the longest collocation frames according to recursive nature and linguistic rules of collocations. Compared with the baseline systems, the proposed method performs significantly better in bigram collocation extraction both in precision and recall. And in extracting collocation frames, the proposed method performs even better with the precision similar to its bigram collocation extraction results. key words: collocation patterns, collocation frames, recursive nature of collocations, collocation rules
Conjugate Gradient Hard Thresholding Pursuit Algorithm for Sparse Signal Recovery

We propose a new iterative greedy algorithm to reconstruct sparse signals in Compressed Sensing. The algorithm, called Conjugate Gradient Hard Thresholding Pursuit (CGHTP), is a simple combination of Hard Thresholding Pursuit (HTP) and Conjugate Gradient Iterative Hard Thresholding (CGIHT). The conjugate gradient method with a fast asymptotic convergence rate is integrated into the HTP scheme that only uses simple line search, which accelerates the convergence of the iterative process. Moreover, an adaptive step size selection strategy, which constantly shrinks the step size until a convergence criterion is met, ensures that the algorithm has a stable and fast convergence rate without choosing step size. Finally, experiments on both Gaussian-signal and real-world images demonstrate the advantages of the proposed algorithm in convergence rate and reconstruction performance.
Proposed RCFS-CARS Framework with Noise Detection and Correction

ABSTRACT Context-Aware Recommender Systems generate more relevant recommendations by adapting them to the specific contextual situation and have become one of the most active research areas in the recommender systems. However, there remains a key issue as how contextual information can be used to create intelligent and useful recommender systems. Use of too many context variables increases dimensionality that leads to loss of accuracy while few context variables fail to bring contextual effects in recommendations. To assist the development and use of context-aware capabilities, we propose a framework, RCFS-CARS that uses influential contexts, make their sets with appropriate relaxation to be applied on different parts of the algorithm. We also propose a strategy to detect the noisy ratings in the datasets and fix them to refine the recommendation results. The experimental results on two datasets reveal that the noise detection and correction process in RCFS-CARS method is much superior and effective in context-aware recommendation scenarios.
Rough intuitionistic type-2 fuzzy c-means clustering algorithm for MR image segmentation

In recent years, the clinical application of magnetic resonance (MR) images is more and more extensive and in-depth. However, image segmentation is a bottleneck to restrict the application of MR imaging in clinic, and the segmentation of brain MR images now is confronted with the presence of uncertainty and noise, and various kinds of algorithms have been proposed to handle this problem. In this study, a hybrid clustering algorithm combined with a new intuitionistic fuzzy factor and local spatial information is proposed, where type-2 fuzzy logic can handle randomness, the rough set can deal with vagueness, and the intuitionistic fuzzy logic can address the external noises. Finally, the experimental tests have been done to demonstrate the superiority of the proposed technique.
Scalable RDMA RPC on Reliable Connection with Efficient Resource Sharing

RDMA provides extremely low latency and high bandwidth to distributed systems. Unfortunately, it fails to scale and suffers from performance degradation when transferring data to an increasing number of targets on Reliable Connection (RC). We observe that the above scalability issue has its root in the resource contention in the NIC cache, the CPU cache and the memory of each server. In this paper, we propose ScaleRPC, an efficient RPC primitive using one-sided RDMA verbs on reliable connection to provide scalable performance. To effectively alleviate the resource contention, ScaleRPC introduces 1) connection grouping to organize the network connections into groups, so as to balance the saturation and thrashing of the NIC cache; 2) virtualized mapping to enable a single message pool to be shared by different groups of connections, which reduces CPU cache misses and improve memory utilization. Such scalable connection management provides substantial performance benefits: By deploying ScaleRPC both in a distributed file system and a distributed transactional system, we observe that it achieves high scalability and respectively improves performance by up to 90% and 160% for metadata accessing and SmallBank transaction processing.
MimosaFTL: Adding Secure and Practical Ransomware Defense Strategy to Flash Translation Layer

Ransomware attacks have become prevalent nowadays due to sudden flourish of cryptocurrencies. Most existing defense strategies for ransomware, however, are vulnerable to privileged ransomware who can compromise the operating system and hence any backup data stored locally. The out-of-place-update and the isolation nature of flash memory storage, for the first time, makes it possible to design a defense strategy which is secure against the privileged ransomware. In this work, we propose MimosaFTL, a secure and practical ransomware defense strategy for mobile computing devices equipped with flash memory as external storage. MimosaFTL is secure against the privileged malware by taking advantage of unique characteristics of flash storage. In addition, it is more practical (compared to prior work) for real-world deployments by: 1) incorporating a fine-grained detection scheme which can detect presence of ransomware accurately; and 2) allowing the victim to efficiently restore the infected external storage to the exact point when the malware starts to perform corruption. Experimental evaluation shows that, MimosaFTL can mitigate ransomware attacks effectively with a small negative impact on both I/O performance and lifetime of flash storage.
ABACaaS: Attribute-Based Access Control as a Service

In recent years, Attribute-Based Access Control (ABAC) has emerged as the desired access control model in scenarios involving sharing of resources across multiple domains. This necessitates organizations using traditional access control models to use ABAC. However, ab initio deployment of ABAC is both cost and time intensive. In this paper, we present ABACaaS - a cloud service that enables any organization to integrate ABAC into their own environment irrespective of the platform they operate in. We show both SaaS as well as PaaS instances of ABACaaS along with results on its performance.
Image denoising by low-rank approximation with estimation of noise energy distribution in SVD domain

Low-rank approximation has shown great potential in various image tasks. It is found that there is a specific functional relationship about singular values between the original image and a series of noisy images, which can be used to construct the singular values of a noise-free image. In this study, the authors propose a novel denoising method based on the above facts and low-rank approximation theory. Firstly, they estimate the noise energy distribution of the group matrix in the singular value decomposition (SVD) domain using the energy characteristics of the image with different noise levels. The energy distribution of the noise is shrunk to obtain the energy distribution of the true signal. Then, based on the optimal energy compaction property of SVD, the low-rank property of matrix is constrained in the SVD domain to obtain the low-rank approximation of the matrix. Moreover, an iterative back projection method is adopted in this study to suppress residual noise. A new noise standard deviation estimation approach, targeted at the back projection process, is proposed to effectively optimise the denoising results during the iteration. Experimental results show that the authors’ method efficiently decreases the noise and achieves comparable denoising performance to the state-of-the-art methods regarding both quantitative measurement and visual effect.
Specification and Analysis of ABAC Policies via the Category-based Metamodel

The Attribute-Based Access Control (ABAC) model is one of the most powerful access control models in use. It subsumes popular models, such as the Role-Based Access Control (RBAC) model, and can also enforce dynamic policies where authorisations depend on values of user, resource or environment attributes. However, in its general form, ABAC does not lend itself well to some operations, such as review queries, and ABAC policies are in general more difficult to specify and analyse than simpler RBAC policies. In this paper we propose a formal specification of ABAC in the category-based metamodel of access control, which adds structure to ABAC policies, making them easier to design and understand. We provide an axiomatic and an operational semantics for ABAC policies, and show how to use them to analyse policies and evaluate review queries.
Latency prediction in 5G for control with deadtime compensation

With the promise of increased responsiveness and robustness of the emerging 5G technology, it is suddenly becoming feasible to deploy latency-sensitive control systems over the cloud via a mobile network. Even though 5G is herald to give lower latency and jitter than current mobile networks, the effect of the delay would still be non-negligible for certain applications. In this paper we explore and demonstrate the possibility of compensating for the unknown and time-varying latency introduced by a 5G mobile network for control of a latency-sensitive plant. We show that the latency from a prototype 5G test bed lacks significant short-term correlation, making accurate latency prediction a difficult task. Further, because of the unknown and time-varying latency our used simple interpolation-based model experiences some troubling theoretical properties, limiting its usability in real world environments. Despite this, we give a demonstration of the strategy which seems to increase robustness in a simulated plant.
Identification of Viable Embryos Using Deep Learning for Medical Image

Identifying viable embryos for implantation is one of the most relevant aspects in assisted reproductive technology. However, embryo selection highly depends on visual examination by embryologists via microscopy, and their evaluations are often subjective. The rapid growth of image processing technology has resulted in increased interest in the use of machine learning methods for embryo selection in in vitro fertilization (IVF) programs. The present study uses deep learning method for the morphological classification of embryos based on medical images. The proposed system is trained and tested on a real data set of 1,310 images from 344 embryos and evaluated by comparison with other traditional machine learning methods to solve similar classification problems. The results indicate that our new deep learning model significantly outperforms other methods. Our work contributes immensely to the fields of assisted reproductive technology, medical image processing, and decision support system design.
Lessons learned and challenges on benchmarking publish-subscribe IoT platforms

The Internet of Things (IoT) emerged simultaneously in various research fields and application domains, leading to the appearence of a multitude of hardware, software technologies, horizontal platforms and data models. Lost in this diversity, adoption decisions are made to a large extent based on familiarity with a technology, or because there is a broad community support for it. In this context, bechmarking different technologies at different horizontal levels would provide a more solid justification for the adoption of a specific technology in a specific context. In this paper, we reflect on our previous work on benchmarking publish-subscribe IoT platforms as a middleware for IoT applications, reporting lessons learned and identifying challenges, thus contributing to the discussion on open topics and relevant downstream work.