Miracle-dz/UniFAR-FTUs huggingface dataset:
Real-Time Static and Dynamic Gesture Recognition Using Mixed Space Features for 3D Virtual World's Interactions

Gesture Recognition is a technology that makes devices such as a computer capable of recognizing and responding to different gestures produced by the human body. With the recent growth of 3D virtual world applications, the demand to improve the gesture recognition method, especially hand gesture recognition, has increased. In this paper, we propose a novel vision-based gesture recognition system for controlling the 3D virtual world based on depth images obtained from the 3D camera device. For the proposed system, we used mix spatial space features consisting of 3D and 2D space features. The finger position in the point cloud represents the 3D space feature and the contour of hand from the images as 2D space feature. To investigate the robustness of our system, we designed 9 gestures including 6 static and 3 dynamic varieties. During experiments, we instruct people to display those gestures and calculate the recognition rate. Our results demonstrate that the proposed system was able to recognize the 9 gestures very well with the average accuracy of 95% for static gestures and 81.34% for dynamic ones.
Deep Recursive Network Embedding with Regular Equivalence

Network embedding aims to preserve vertex similarity in an embedding space. Existing approaches usually define the similarity by direct links or common neighborhoods between nodes, i.e. structural equivalence. However, vertexes which reside in different parts of the network may have similar roles or positions, i.e. regular equivalence, which is largely ignored by the literature of network embedding. Regular equivalence is defined in a recursive way that two regularly equivalent vertexes have network neighbors which are also regularly equivalent. Accordingly, we propose a new approach named Deep Recursive Network Embedding (DRNE) to learn network embeddings with regular equivalence. More specifically, we propose a layer normalized LSTM to represent each node by aggregating the representations of their neighborhoods in a recursive way. We theoretically prove that some popular and typical centrality measures which are consistent with regular equivalence are optimal solutions of our model. This is also demonstrated by empirical results that the learned node representations can well predict the indexes of regular equivalence and related centrality scores. Furthermore, the learned node representations can be directly used for end applications like structural role classification in networks, and the experimental results show that our method can consistently outperform centrality-based methods and other state-of-the-art network embedding methods.
Scaling Datacenter Accelerators with Compute-Reuse Architectures

Hardware specialization is commonly used in datacenters to ameliorate the nearing end of CMOS technology scaling. While offering superior performance and energy-efficiency returns compared to general-purpose processors, specialized accelerators are bound to the same device technology constraints, and are thus prone to similar limitations in the future. Once technology scaling plateaus, accelerator and application tuning will reach a point of near-optimum, with no clear direction for further improvements. Emerging non-volatile memory (NVM) technologies follow different scaling trends due to different physical properties and manufacturing techniques. NVMs have inspired recent efforts of innovation in computer systems, as they possess appealing qualities such as high capacity and low energy. We present the COmpute-REuse Accelerators (COREx) architecture that shifts computations from the scalability-hindered transistor-based logic towards the continuing-to-scale storage domain. COREx leverages datacenter redundancy by integrating a storage layer together with the accelerator processing layer. The added layer stores the outcomes of previous accelerated computations. The previously computed results are reused in the case of recurring computations, thus eliminating the need to re-compute them. We designed COREx as a combination of an accelerator and specialized storage layer using emerging memory technologies, and evaluated it on a set of datacenter workloads. Our results show that, when integrated with a well-tuned accelerator, COREx achieves an average speedup of 6.4x and average savings of 50% in energy and 63% in energy-delay product. We expect further increase in gains in the future, as memory technologies continue to improve steadily.
Microarray gene expression profiling in colorectal (HCT116) and hepatocellular (HepG2) carcinoma cell lines treated with Melicope ptelefolia leaf extract reveals transcriptome profiles exhibiting anticancer activity

Background We have previously reported anticancer activities of Melicope ptelefolia (MP) leaf extracts on four different cancer cell lines. However, the underlying mechanisms of actions have yet to be deciphered. In the present study, the anticancer activity of MP hexane extract (MP-HX) on colorectal (HCT116) and hepatocellular carcinoma (HepG2) cell lines was characterized through microarray gene expression profiling. Methods HCT116 and HepG2 cells were treated with MP-HX for 24 hr. Total RNA was extracted from the cells and used for transcriptome profiling using Applied Biosystem GeneChip™ Human Gene 2.0 ST Array. Gene expression data was analysed using an Applied Biosystems Expression Console and Transcriptome Analysis Console software. Pathway enrichment analyses was performed using Ingenuity Pathway Analysis (IPA) software. The microarray data was validated by profiling the expression of 17 genes through quantitative reverse transcription PCR (RT-qPCR). Results MP-HX induced differential expression of 1,290 and 1,325 genes in HCT116 and HepG2 cells, respectively (microarray data fold change, MA_FC ≥ ±2.0). The direction of gene expression change for the 17 genes assayed through RT-qPCR agree with the microarray data. In both cell lines, MP-HX modulated the expression of many genes in directions that support antiproliferative activity. IPA software analyses revealed MP-HX modulated canonical pathways, networks and biological processes that are associated with cell cycle, DNA replication, cellular growth and cell proliferation. In both cell lines, upregulation of genes which promote apoptosis, cell cycle arrest and growth inhibition were observed, while genes that are typically overexpressed in diverse human cancers or those that promoted cell cycle progression, DNA replication and cellular proliferation were downregulated. Some of the genes upregulated by MP-HX include pro-apoptotic genes (DDIT3, BBC3, JUN), cell cycle arresting (CDKN1A, CDKN2B), growth arrest/repair (TP53, GADD45A) and metastasis suppression (NDRG1). MP-HX downregulated the expression of genes that could promote anti-apoptotic effect, cell cycle progression, tumor development and progression, which include BIRC5, CCNA2, CCNB1, CCNB2, CCNE2, CDK1/2/6, GINS2, HELLS, MCM2/10 PLK1, RRM2 and SKP2. It is interesting to note that all six top-ranked genes proposed to be cancer-associated (PLK1, MCM2, MCM3, MCM7, MCM10 and SKP2) were downregulated by MP-HX in both cell lines. Discussion The present study showed that the anticancer activities of MP-HX are exerted through its actions on genes regulating apoptosis, cell proliferation, DNA replication and cell cycle progression. These findings further project the potential use of MP as a nutraceutical agent for cancer therapeutics.
Decision Variance in Online Learning

Online learning has classically focused on the expected behaviour of learning policies. Recently, risk-averse online learning has gained much attention. In this paper, a risk-averse multi-armed bandit problem where the performance of policies is measured based on the mean-variance of the rewards is studied. The variance of the rewards depends on the variance of the underlying processes as well as the variance of the player's decisions. The performance of two existing policies is analyzed and new fundamental limitations on risk-averse learning is established. In particular, it is shown that although an $\mathcal{O}(\log T)$ distribution-dependent regret in time $T$ is achievable (similar to the risk-neutral setting), the worst-case (i.e. minimax) regret is lower bounded by $\Omega(T)$ (in contrast to the $\Omega(\sqrt{T})$ lower bound in the risk-neutral setting). The lower bound results are even stronger in the sense that they are proven for the case of online learning with full feedback.
Algorithm Selection for Collaborative Filtering: the influence of graph metafeatures and multicriteria metatargets

To select the best algorithm for a new problem is an expensive and difficult task. However, there are automatic solutions to address this problem: using Metalearning, which takes advantage of problem characteristics (i.e. metafeatures), one is able to predict the relative performance of algorithms. In the Collaborative Filtering scope, recent works have proposed diverse metafeatures describing several dimensions of this problem. Despite interesting and effective findings, it is still unknown whether these are the most effective metafeatures. Hence, this work proposes a new set of graph metafeatures, which approach the Collaborative Filtering problem from a Graph Theory perspective. Furthermore, in order to understand whether metafeatures from multiple dimensions are a better fit, we investigate the effects of comprehensive metafeatures. These metafeatures are a selection of the best metafeatures from all existing Collaborative Filtering metafeatures. The impact of the most representative metafeatures is investigated in a controlled experimental setup. Another contribution we present is the use of a Pareto-Efficient ranking procedure to create multicriteria metatargets. These new rankings of algorithms, which take into account multiple evaluation measures, allow to explore the algorithm selection problem in a fairer and more detailed way. According to the experimental results, the graph metafeatures are a good alternative to related work metafeatures. However, the results have shown that the feature selection procedure used to create the comprehensive metafeatures is is not effective, since there is no gain in predictive performance. Finally, an extensive metaknowledge analysis was conducted to identify the most influential metafeatures.
Website Fingerprinting Attack Mitigation Using Traffic Morphing

Website fingerprinting attacks attempt to identify the website visited in anonymized and encrypted network traffic, that is, even if a user is using Tor and HTTPS. These attacks have been shown to be effective. Mitigations have been proposed which decreased the accuracy of the attacks from about 90% to about 20%. We propose a new mitigation technique based on traffic morphing and clustering. The intuition is that a lot of websites, by nature, are similar and can be clustered together. It is then easier and more efficient to make that whole cluster look exactly the same by using traffic morphing, rather than adding noise to make all websites look similar. All the websites in a cluster, thus, would become indistinguishable. There are many ways to perform traffic morphing. As a proof of concept, we used biggest, which means that all websites in a cluster will look exactly like the biggest website (in terms of network packet size) of that cluster. In simulating our proposed approach, the fingerprinting accuracy dropped from 70% to less than 1%.
SDN-enabled Infrastructure Sharing in Emerging Markets: CapEx/OpEx Savings Overview and Quantification

The paper quantifies the importance of network resource sharing in leveraging Software Defined Networking (SDN) in mobile network infrastructure and reducing operation costs between multiple operators, especially critical to extend broadband access in less populated and thus less profitable rural areas. The paper starts with an overview of the concept of infrastructure sharing and different levels of sharing, such as RAN, backhaul and core sharing, and then attempts to quantify the economic impact of sharing. The impact is estimated in terms of how long it takes to recover CapEx investments and make a network profitable. This is illustrated utilizing the use case example of the South African environment that full sharing may allow an order of magnitude reduction in time needed to reach profitability, which may allow for profitable operation in rural areas and easier entry of smaller operators into market.
Maintaining Third-Party Libraries through Domain-Specific Category Recommendations

Proper maintenance of third-party libraries contributes toward sustaining a healthy project, mitigating the risk it becoming outdated and obsolete. In this paper, we propose domain-specific categories (i.e., grouping of libraries that perform similar functionality) in library recommendations that aids in library maintenance. Our empirical study covers 2,511 GitHub projects and 150 domain-specific categories of Java libraries. Our results show that a system uses up to six different categories in their dependencies. Furthermore, recommending domain-specific categories is practical (i.e., with an accuracy between 66% to 81% for multiple categories) and its suggestion of libraries within that domain is comparable to existing techniques.
SIPRAD: A Radiotherapy Planning System (RTPS)

Cancer is one type of lethal disease that needs an immediate diagnosis and treatment, demanding the use of advanced technologies. External Radiotherapy is the most common treatment used in the world. Radiotherapy Planning Systems (RTPS) is a software suite used for delineation of organs and calculation of irradiation doses and is essential to ensure the accuracy of oncologic treatment in clinics and hospitals. This works presents a type of RTPS, named SIPRAD, developed for public hospitals reality in Brazil with the assistance of the doctors. SIPRAD architecture uses a number of management and operational tools for all radiotherapy treatment process. One of the main contributions of this work is its interoperable infrastructure capable of communicating with all health systems and devices. Another contribution is a drawing tool, which is able to perform an automatic contour delineation in organs at risk and tumors volumes. Additionally also extends the use of tomography slices in grayscale images applied in three plans: axial, coronal and sagittal. SIPRAD automatic contour delineation saves up 75% of the time for drawings in three different types of regions body exams. Finally, SIPRAD was tested in a set of oncologic exams provided by the University of São Paulo Clinical Hospital (HCRP and HC-USP) in Brazil obtained from patients with different conditions of ethnology, ages, tumor severities and body regions.
Static Software Metrics for Reliability and Maintainability

This paper identifies a small, essential set of static software code metrics linked to the software product quality characteristics of reliability and maintainability and to the most commonly identified sources of technical debt. An open-source plug-in is created for the Understand code analysis tool that calculates and visualizes these metrics. The plug-in was developed as a first step in an ongoing project aimed at applying case-based reasoning to the issue of software product quality.
Towards an Open Government Data Comparative Model

Governments around the world have been increasing their practices for open government data (OGD). After the launch of the initiative for the Open Government Partnership (OGP) many countries have developed legal arrangements, organizational transformations and cultural challenges in order to implement some practices of OGD in their Public Administrations. However very few have compared their results and experiences in a systematic way. The purpose of this paper is to introduce a first step to develop a comparative framework of OGD. Using the experience of Mexico and Russia governments on implementation we describe this experience of country comparison in order to foster the OGD implementation across the world.
Cognified Distributed Computing

Cognification – the act of transforming ordinary objects or processes into their intelligent counterparts through Data Science and Artificial Intelligence – is a disruptive technology that has been revolutionizing disparate fields ranging from corporate law to medical diagnosis. Easy access to massive data sets, data analytics tools and High-Performance Computing (HPC) have been fueling this revolution. In many ways, cognification is similar to the electrification revolution that took place more than a century ago when electricity became a ubiquitous commodity that could be accessed with ease from anywhere in order to transform mechanical processes into their electrical counterparts. In this paper, we consider two particular forms of distributed computing – Data Centers and HPC systems – and argue that they are ripe for cognification of their entire ecosystem, ranging from top-level applications down to low-level resource and power management services. We present our vision for what "Cognified Distributed Computing" might look like and outline some of the challenges that need to be addressed and new technologies that need to be developed in order to make it a reality. In particular, we examine the role cognification can play in tackling power consumption, resiliency and management problems in these systems. We describe intelligent software-based solutions to these problems powered by on-line predictive models built from streamed real-time data. While we cast the problem and our solutions in the context of large Data Centers and HPC systems, we believe our approach to be applicable to distributed computing in general. We believe that the traditional systems research agenda has much to gain by crossing discipline boundaries to include ideas and techniques from Data Science, Machine Learning and Artificial Intelligence.
On Verifying and Assuring the Cloud SLA by Evaluating the Performance of SaaS Web Services Across Multi-cloud Providers

Software-as-a-service (SaaS) cloud web services are delivered by cloud providers to cloud customers on a pay-peruse model. Service level agreements (SLAs) are used to define the quality of the provided services. Unfortunately, there is no standard mechanism or an automatic way which exist to verify and assure that delivered services satisfy the signed SLA agreement and impede the possibility to measure accurately the Quality of Service (QoS). In this abstract, we offer a framework of steps to verify and assure the SLA compliance of Web Services offered across several Cloud providers. Our framework targets also the evaluation of the QoS of the SaaS Web Services (WSs). In this context, our framework aims at quantifying in a fair and by stealth way the performance and scalability of the delivered WS. The framework defines a set of Common performance metrics handled by a set of agents for measuring the behaviour of cloud applications on top of a given CSP. The premier results from this framework are summarized by the performance metrics monitoring, modelling and sensitivity analysis of the models' parameters. This framework opens novel perspectives for assessing the SLA compliance of Cloud providers and QoS evaluation.
Low-Power Noise-Immune Nanoscale Circuit Design Using Coding-Based Partial MRF Method

Reliability is one of the major concerns for ultralow power circuit designs. Markov random field (MRF) techniques have been applied to logic circuits to resist random noise when operating under ultralow supply voltage or sub-threshold voltage. Although conventional MRF networks can be easily mapped onto simple logic circuits, it becomes difficult when the circuits are large and complex. In this paper, we present a general coding-based partial MRF (CPMRF) method for multi-logic operations in one basic unit, which is referred to as a CPMRF pair. A CPMRF pair saves circuit area by sharing a common MRF network. It also inherits noise immunity from the MRF theory while obtaining noise immunity from the coding structure as a combination of robust “1s” and “0s. ” The resulting architectures become more cost effective than conventional ones. To validate the performance of our proof-of-concept design, we fabricated a carry-lookahead adder implemented by the proposed CPMRF pairs using IBM 130-nm CMOS technology. Measurement results indicate that the CPMRF CLA can achieve high noise tolerance with 20% improvement while occupying 37.7% less area and reducing power consumption by 93% compared with the master-and-slave MRF CLA design.
DHL: Enabling Flexible Software Network Functions with FPGA Acceleration

Network function virtualization (NFV) aims to run software network functions (NFs) in commodity servers. As CPU is general-purpose hardware, one has to use many CPU cores to handle complex packet processing at line rate. Owing to its performance and programmability, FPGA has emerged as a promising platform for NFV. However, the programmable logic blocks on an FPGA board are limited and expensive. Implementing the entire NFs on FPGA is thus resource-demanding. Further, FPGA needs to be reprogrammed when the NF logic changes which can take hours to synthesize the code. It is thus inflexible to use FPGA to implement the entire NFV service chain. We present dynamic hardware library (DHL), a novel CPU-FPGA co-design framework for NFV with both high performance and flexibility. DHL employs FPGA as accelerators only for complex packet processing. It abstracts accelerator modules in FPGA as a hardware function library, and provides a set of transparent APIs for developers. DHL supports running multiple concurrent software NFs with distinct accelerator functions on the same FPGA and provides data isolation among them. We implement a prototype of DHL with Intel DPDK. Experimental results demonstrate that DHL greatly reduces the programming efforts to access FPGA, brings significantly higher throughput and lower latency over CPU-only implementation, and minimizes the CPU resources.
Assessing Test Case Prioritization on Real Faults and Mutants

Test Case Prioritization (TCP) is an important component of regression testing, allowing for earlier detection of faults or helping to reduce testing time and cost. While several TCP approaches exist in the research literature, a growing number of studies have evaluated them against synthetic software defects, called mutants. Hence, it is currently unclear to what extent TCP performance on mutants would be representative of the performance achieved on real faults. To answer this fundamental question, we conduct the first empirical study comparing the performance of TCP techniques applied to both real-world and mutation faults. The context of our study includes eight well-studied TCP approaches, 35k+ mutation faults, and 357 real-world faults from five Java systems in the Defects4J dataset. Our results indicate that the relative performance of the studied TCP techniques on mutants may not strongly correlate with performance on real faults, depending upon attributes of the subject programs. This suggests that, in certain contexts, the best performing technique on a set of mutants may not be the best technique in practice when applied to real faults. We also illustrate that these correlations vary for mutants generated by different operators depending on whether chosen operators reflect typical faults of a subject program. This highlights the importance, particularly for TCP, of developing mutation operators tailored for specific program domains.