Miracle-dz/UniFAR-FTUs huggingface dataset:
Poly: Efficient Heterogeneous System and Application Management for Interactive Applications

QoS-sensitive workloads, common in warehousescale datacenters, require a guaranteed stable tail latency percentile response latency) of the service. Unfortunately, the system load (e.g., RPS) fluctuates drastically during daily datacenter operations. In order to meet the maximum system RPS requirement, datacenter tends to overprovision the hardware accelerators, which makes the datacenter underutilized.Therefore, the throughput and energy efficiency scaling of the current accelerator-outfitted datacenter are very expensive for QoS-sensitive workloads. To overcome this challenge, this work introduces Poly, an OpenCL based heterogeneous system optimization framework that targets to improve the overall throughput scalability and energy proportionality while guaranteeing the QoS by efficiently utilizing GPUs and FPGAs based accelerators within datacenter. Poly is mainly composed of two phases. At compile-time, Poly automatically captures the parallel patterns in the applications and explores a comprehensive design space within and across parallel patterns. At runtime, Poly relies on a runtime kernel scheduler to judiciously make the scheduling decisions to accommodate the dynamic latency and throughput requirements. Experiments using a variety of cloud QoS-sensitive applications show that Poly improves the energy proportionality by 23%(17%) without sacrificing the QoS compared to the state-of-the-art GPU (FPGA) solution, respectively. Keywords-Heterogeneous; GPU; FPGA; Performance Optimization;
Oral Administration of Compound Probiotics Improved Canine Feed Intake, Weight Gain, Immunity and Intestinal Microbiota

Probiotics have been used successfully to promote human and animal health, but only limited studies have focused on using probiotics to improve the health of hosts of different age. Canine microbiome studies may be predictive of results in humans because of the high structural and functional similarity between dog and human microbiomes. A total of 90 dogs were divided into three groups based on dog age (elderly group, n = 30; young group, n = 24; and training group, n = 36). Each group was subdivided into two subgroups, with and without receiving daily probiotic feed additive. The probiotic feed additive contained three different bacterial strains, namely Lactobacillus casei Zhang, Lactobacillus plantarum P-8, and Bifdobacterium animalis subsp. lactis V9. Serum and fecal samples were collected and analyzed at four different time points, i.e., days 0, 30, and 60 of probiotic treatment, and 15 days after ceasing probiotic treatment. The results demonstrated that probiotics significantly promoted the average daily feed intake of the elderly dogs (P < 0.01) and the average daily weight gain of all dogs (P < 0.05), enhanced the level of serum IgG (P < 0.001), IFN-α (P < 0.05), and fecal SIgA (P < 0.001), while reduced the TNF-α (P < 0.05). Additionally, probiotics could change the gut microbial structure of elderly dogs and significantly increased beneficial bacteria (including some Lactobacillus species and Faecalibacterium prausnitzii) and decreased potentially harmful bacteria (including Escherichia coli and Sutterella stercoricanisin), and the elderly dogs showed the strongest response to the probiotics; the relative abundance of some of these species correlated with certain immune factors and physiological parameters, suggesting that the probiotic treatment improved the host health and enhanced the host immunity by stimulating antibody and cytokine secretion through regulating canine gut microbiota. Furthermore, the gut microbiota of the elderly dogs shifted toward a younger-like composition at day 60 of probiotic treatment. Our findings suggested that the probiotic treatment effects on canine health and immunity were age-related and have provided interesting insights into future development of probiotic-based strategies to improve animal and human health.
Darwin-WGA: A Co-processor Provides Increased Sensitivity in Whole Genome Alignments with High Speedup

Whole genome alignment (WGA) is an indispensable tool in comparative genomics to study how different lifeforms have been shaped by evolution at the molecular level. Existing software whole genome aligners require several CPU weeks to compare a pair of mammalian genomes and still miss several biologically-meaningful, high-scoring alignment regions. These aligners are based on the seed-filter-and-extend paradigm with an ungapped filtering stage. Ungapped filtering is responsible for the low sensitivity of these aligners but is used because it is 200× faster than performing gapped alignment, using dynamic programming, in software. In this paper, we show that both performance and sensitivity can be greatly improved by using a hardware accelerator for WGA. Using the genomes of two roundworms (C. elegans and C. Briggsae) and four fruit flies (D. melanogaster, D. simulans, D. yakuba, and D. pseudoobscura), we show that replacing ungapped filtering with gapped filtering increases the number of matching base-pairs in alignments by up to 3×. Our accelerator, Darwin-WGA, is the first hardware accelerator for whole genome alignment and accelerates the gapped filtering stage. Darwin-WGA also employs GACT-X, a novel algorithm used in the extension stage to align arbitrarily long genome sequences using a small on-chip memory, that provides better quality alignments at 2× improvement in memory and speed over the previously published GACT algorithm. Implemented on an FPGA, Darwin-WGA provides up to 24× improvement (performance/$) in WGA over iso-sensitive software. An ASIC implementation of the proposed architecture on TSMC 40nm technology takes around 43W power with 36mm area. It achieves up to 10× performance/watt improvement on whole genome alignments over state-of-the-art software at higher sensitivity, and up to 1,500× performance/watt improvement compared to iso-sensitive software. Darwin-WGA is released under open-source MIT license and is available from https://github.com/gsneha26/Darwin-WGA. Keywords-Co-processor, Comparative Genomics, Whole Genome Alignment, Gapped Filtering
NAND-Net: Minimizing Computational Complexity of In-Memory Processing for Binary Neural Networks

—Popular deep learning technologies suffer from memory bottlenecks, which signiﬁcantly degrade the energy-efﬁciency, especially in mobile environments. In-memory processing for binary neural networks (BNNs) has emerged as a promising solution to mitigate such bottlenecks, and various relevant works have been presented accordingly. However, their performances are severely limited by the overheads induced by the modiﬁcation of the conventional memory architectures. To alleviate the performance degradation, we propose NAND-Net, an efﬁcient architecture to minimize the computational complexity of in-memory processing for BNNs. Based on the observation that BNNs contain many redundancies, we decomposed each convolution into sub-convolutions and eliminated the unnecessary operations. In the remaining convolution, each binary multiplication (bitwise XNOR) is replaced by a bitwise NAND operation, which can be implemented without any bit cell modiﬁcations. This NAND operation further brings an opportunity to simplify the subsequent binary accumulations (popcounts). We reduced the operation cost of those popcounts by exploiting the data patterns of the NAND outputs. Compared to the prior state-of-the-art designs, NAND-Net achieves 1.04-2.4x speedup and 34-59% energy saving, thus making it a suitable solution to implement efﬁcient in-memory processing for BNNs.
Stretch: Balancing QoS and Throughput for Colocated Server Workloads on SMT Cores

—In a drive to maximize resource utilization, today’s datacenters are moving to colocation of latency-sensitive and batch workloads on the same server. State-of-the-art deployments, such as those at Google, colocate such diverse workloads even on a single SMT core. This form of aggressive colocation is afforded by virtue of the fact that a latency-sensitive service operating below its peak load has signiﬁcant slack in its response latency with respect to the QoS target. The slack affords a degradation in single-thread performance, which is inevitable under SMT colocation, without compromising QoS targets.This work makes the observation that many batch applications can greatly beneﬁt from a large instruction window to uncover ILP and MLP. Under SMT colocation, conventional wisdom holds that individual hardware threads should be limited in their ability to acquire and hold a disproportionately large share of microarchitectural resources so as not to compromise the performance of a co-running thread. We show that the performance slack inherent in latency-sensitive workloads operating at low to moderate load makes it safe to shift microarchitectural resources to a co-running batch thread without compromising QoS targets. Based on this insight, we introduce Stretch, a simple ROB partitioning scheme that is invoked by system software to provide one hardware thread with a much larger ROB partition at the expense of another thread. When Stretch is enabled for latency-sensitive workloads operating below their peak load on an SMT core, co-running batch applications gain 13% of performance on average (30% max) over a baseline SMT colocation and without compromising QoS constraints.
IoT Enabled Real Time Bolt based Indoor Air Quality Monitoring System

Indoor air pollution results in substantially harmful effects on human health. In this paper, an Bolt based Internet of Things (IoT) system has been devised to monitor in real time the basic pollutants like Carbon dioxide, Carbon mono oxide, Particulate matter, in indoor environment of university premises. Bolt based IoT system provides pollution level information directly to smart device in real time. In the later segment of the paper, authors have also suggested the measures to improve the quality of air for better health of the students that may positively impact their academic performance.
The Task of Instance Segmentation of Mineral Grains in Digital Images of Rock Samples (Thin Sections)

The paper considers developing the method for instance segmentation of mineral grains in thin section images of sandstone. This task involves the segmentation of quasi-convex objects without occlusions. Most often grains are tightly packed without clear boundaries. In some cases, the abstract division of image areas into grains and cement may be ambiguous. To tackle this problems, we propose a flexible and robust solution. The algorithm is based on a cascade of two fully-convolution neural networks (FCNN). The first model is designed to localize objects by restoring the normalized distance transform. The second model is used to predict binary mask from specific markers for the localized object. We proposed a technique for independent models optimization from the cascade. This allowed us to construct an algorithm with a relatively small amount of the training sample (with 9000 instances). The implemented solution was successfully tested on validation data. High quality model predictions allowed us to significantly increase the size of the training sample and use algorithm for morphological features extraction.
E-learning Platforms Security Issues and Vulnerability Analysis

E-Learning is a fairly a new concept of learning electronically, mostly through the internet. Web applications and E-learning Platforms are important and billions of users rely for performing routine activities. User-friendliness, 24/7 availability and ubiquitous of web applications have made them vulnerable. A unpretentious implementation fault in the application could allow an invader to steal sensitive information and perform adversary actions, and hence it is important to secure web applications from attacks. Defensive mechanisms for securing web applications from the flaws have received attention from both academia and industry. In this paper, all available modern-day open-source learning management systems (E-Learning software) are evaluated for security issues and vulnerabilities. Those platforms were thoroughly tested using two web vulnerability scanners due to the big variances among security scanners. The key aim was to assess this software category illustrating valuable conclusions on their security level today, since such popular categories of software should above all ensure that security is a must, something that seems to be neglected: until now this category of software was not tested in the literature for weaknesses; The major contribution of this paper is to inform the educational community about the prevailing vulnerabilities and the probable risks and flaws with the significant results, and offer a valuable contribution to the developing communities of the above platforms by recommending a significant security model. This security model can be easily adapted to the needs of every different e-learning platform or even web platforms in over-all since this is an almost non-existing topic in the current scientific literature.
Embedded Representation of Relation Words with Visual Supervision

Word representation learned from the analysis of natural language does not usually reflect the true semantics of words. The paper proposes a new method, named Visually Supervised Word2Vec (VS-Word2Vec) model to achieve the representation of the relation words that are important in knowledge related tasks. Our method first computes the visual feature vector of relation words based on deep networks, and then achieve the visual similarity matrix for all relation words, which we think reflects their true semantics. VSWord2Vec model then combines the visual similarity and the CBOW and builds an optimization problem to jointly learn the word vector representation. Therefore, VS-Word2Vec fuses the visual modality and natural language together. Experiments implemented over the public datasets demonstrate that VSWord2Vec model really changes the distribution of word representation and achieves more effective results in describing their true semantics than CBOW model. Keywords-vector representation, visual supervision, deep learning.
POWERT Channels: A Novel Class of Covert CommunicationExploiting Power Management Vulnerabilities

To be able to meet demanding application performance requirements within a tight power budget, runtime power management must track hardware activity at a very fine granularity in both space and time. This gives rise to sophisticated power management algorithms, which need the underlying system to be both highly observable (to be able to sense changes in instantaneous power demand timely) and controllable (to be able to react to changes in instantaneous power demand timely). The end goal is allocating the power budget, which itself represents a very critical shared resource, in a fair way among active tasks of execution. Fundamentally, if not carefully managed, any system-wide shared resource can give rise to covert communication. Power budget does not represent an exception, particularly as systems are becoming more and more observable and controllable. In this paper, we demonstrate how power management vulnerabilities can enable covert communication over a previously unexplored, novel class of covert channels which we will refer to as POWERT channels. We also provide a comprehensive characterization of the POWERT channel capacity under various sharing and activity scenarios. Our analysis based on experiments on representative commercial systems reveal a peak channel capacity of 121.6 bits per second (bps). Keywords-covert channels; power management; power headroom modulation.
Edge Computing Sever Selection in Fog Radio Access Networks

In this paper, we consider a fog radio access (F-RAN) network formed by two different kinds of node, namely the user and calculator (i.e., the edge computing sever), respectively. It is assumed that the users and calculators follows two independent homogeneous Poisson point processes (HPPPs). Further, it is assumed that the arrival of the computing tasks at each user and the departure of the computing tasks at each calculator follow two Poisson processes with rate ηu and ηc, respectively. That is, the time between successive arrivals at each user or departures at each calculator are independent exponential random variables with mean given by $\frac{1}{{{\eta _u}}}$ or $\frac{1}{{{\eta _c}}}$. Time is divided into slots. At the beginning each time slot, each user is designed to offload the computing tasks generated in the last time slot to their nearby calculators within a distance of Rd for data processing. Different from the random-chosen based calculator selection schemes studied in the previous works, in this paper, we consider a prediction based calculator selection strategy to enhance the delay performance of the F-RAN. Particularly, under the proposed prediction based calculator selection strategy, we estimate the network status in the next time slot, and use which as the criterion for computing server selection and tasks offloading. It is shown through both the analysis and simulations that the proposed prediction based calculator selection strategy outperforms the random-chosen based calculator selection protocol in terms of the successful offloading probability.
Application of Color Filter Adjustment and K-Means Clustering Method in Lane Detection for Self-Driving Cars

The lane detection is a crucial key fact for advanced driving assistance systems. Hence, it is an active filed of research in recent years. The challenges to confront is to deal with various scenarios, such as when shadow interference or inconsistency of the road color and texture occur. This study propose to use a combination of color filters and a K-Means clustering filter to reduce the interfering noise. The result of our experiments show significant improvement in the noise robustness in lane detection.
The Accelerator Wall: Limits of Chip Specialization

Specializing chips using hardware accelerators has become the prime means to alleviate the gap between the growing computational demands and the stagnating transistor budgets caused by the slowdown of CMOS scaling. Much of the benefits of chip specialization stems from optimizing a computational problem within a given chip’s transistor budget. Unfortunately, the stagnation of the number of transistors available on a chip will limit the accelerator design optimization space, leading to diminishing specialization returns, ultimately hitting an accelerator wall. In this work, we tackle the question of what are the limits of future accelerators and chip specialization? We do this by characterizing how current accelerators depend on CMOS scaling, based on a physical modeling tool that we constructed using datasheets of thousands of chips. We identify key concepts used in chip specialization, and explore case studies to understand how specialization has progressed over time in different applications and chip platforms (e.g., GPUs, FPGAs, ASICs)1. Utilizing these insights, we build a model which projects forward to see what future gains can and cannot be enabled from chip specialization. A quantitative analysis of specialization returns and technological boundaries is critical to help researchers understand the limits of accelerators and develop methods to surmount them. Keywords-Accelerator Wall; Moore’s Law; CMOS Scaling
Compressed Sensing for Energy and Bandwidth Starved IoT Applications

Ensuring security through the use of video surveillance cameras at public places is becoming attractive these days, thanks to the efficient compression, transmission and storage schemes. To up-scale the surveillance mechanism to large sensor networks, it is imperative that the applications become compatible to wireless sensor networks using Internet of Things (IoT) infrastructure. IoT nodes are generally energy and bandwidth-limited owing to their small size and large scale deployment. Therefore, any image/video acquisition application using IoT infrastructure should function within these constraints. Compressed sensing (CS) is one such paradigm that uses simultaneous sensing and compression and provides a technique for efficient image/video acquisition. This paper investigates the use of compressed sensing for image acquisition in IoT based applications that suffer from energy, bandwidth and storage limitations.
Using Adaptive Nested Mesh Code HydroBox3D for Numerical Simulation of Type Ia Supernovae: Merger of Carbon-Oxygen White Dwarf Stars, Collapse, and Non-Central Explosion

The paper presents the results of mathematical modeling of the three main stages of the explosion of supernovae of type Ia (SNIa): merging of white dwarfs, collapse and noncentral explosion. In the fusion problem shown the general fall mechanism of the satellite star onto the central star. At the modeling of the collapse process, the behavior of various types of energies is investigated. Also shown is the qualitative dynamics of a noncentral of a star explosion, whose source is the characteristic value of the energy released by the reaction C + C. The simulation was carried out using the original HydroBox3D code, in which the concept of adaptive nested mesh is implemented.
NoMap: Speeding-Up JavaScript Using Hardware Transactional Memory

—Scripting languages’ inferior performance stems from compilers lacking enough static information. To address this limitation, they use JIT compilers organized into multiple tiers, with higher tiers using proﬁling information to generate high-performance code. Checks are inserted to detect incorrect assumptions and, when a check fails, execution transfers to a lower tier. The points of potential transfer between tiers are called Stack Map Points (SMPs) . They require a consistent state in both tiers and, hence, limit code optimization across SMPs in the higher tier. This paper examines the code generated by a state-of-the-art JavaScript compiler and ﬁnds that the code has a high frequency of SMPs. These SMPs rarely cause execution to transfer to lower tiers. However, both the optimization-limiting effect of the SMPs, and the overhead of the SMP-guarding checks contribute to scripting languages’ low performance. To tackle this problem, we extend the compiler to generate hardware transactions around SMPs, and perform simple within-transaction optimizations enabled by transactions. We target emerging lightweight HTM systems and call our changes NoMap . We evaluate NoMap on the SunSpider and Kraken suites. We ﬁnd that NoMap lowers the instruction count by an average of 14.2% and 11.5%, and the execution time by an average of 16.7% and 8.9%, for SunSpider and Kraken, respectively.
Tailoring of Band Gap in Manganese doped Sodium Hexa-titanate

Pure and Manganese doped Sodium Hexa-titanate (Na2 Ti6 O13) were synthesized using solid state reaction method. X-ray diffraction pattern reveals single phase and exhibit monoclinic structure of pure and doped samples. Morphologies of all synthesized materials have been studied using SEM. Absorption spectra of these samples have also been recorded and the same have been used to calculate the energy band gap using Tauc relation. The band gap lies in the range 3.71eV to 4.05 eV.
Towards Behavioural Cloning for Autonomous Driving

This paper proposes an off-policy imitation learning methodology for autonomous driving using a doubly-deep recurrent convolutional architecture that learns compositional representations in both space and time domains. The architecture has been referred to as NAVNet (Navigation Network) and is end-to-end trainable. The recurrent long-term models are directly connected with the visual convolutional models. The models can be trained together to learn both temporal dynamics as well as the convolutional perceptual representations. The approach is non-data driven in nature and the system learns a regression-based mapping function between input images and steering angle. Results presented in this research indicate distinct advantages of the proposed LRCN model over the state-of-the-art deep learning techniques for autonomous navigation.