Reviewerly/exHarmony huggingface dataset:
P-PFC: Reducing Tail Latency with Predictive PFC in Lossless Data Center Networks

Remote Direct Memory Access(RDMA) technology rapidly changes the landscape of nowadays datacenter applications. Congestion control for RDMA networking is a critical challenge. As an end-to-end layer 3 congestion control mechanism, Datacenter QCN (DCQCN) alleviates the unfairness and head-of-the-line blocking problems of Priority-based Flow Control (PFC). However, a lossless network does not guarantee low latency even with DCQCN enabled. When network congestion happens, switch queues still build-up due to the response latency of end-to-end solutions. In this article, we propose Predictive PFC (P-PFC) to reduce tail latency in RDMA networks. P-PFC monitors the derivative of buffer occupation, predicts the happening of PFC trigger in the future, and proactively triggers PFC pause in advance. The benefit is that buffer usage can be maintained at a low level, hence the tail latency can be controlled. Preliminary evaluation results demonstrate that P-PFC can reduce tail latency by more than half of that in standard PFC in many scenarios, without hurting the throughput and average latency. P-PFC can also protect innocent flows compared with standard PFC according to our experiments. To our best knowledge, this is the first work of using derivative to improve PFC in lossless RDMA networks.
Orthogonal canonical correlation analysis and applications

Canonical correlation analysis (CCA) is a cornerstone of linear dimensionality reduction techniques that jointly maps two datasets to achieve maximal correlation. CCA has been widely used in applications for capturing data features of interest. In this paper, we establish a range constrained orthogonal CCA (OCCA) model and its variant and apply them for three data analysis tasks of datasets in real-life applications, namely unsupervised feature fusion, multi-target regression and multi-label classification. Numerical experiments show that the OCCA and its variant produce superior accuracy compared to the traditional CCA.
Calling for More Consistency, Refinement, and Critical Consideration in the Use of Syntactic Complexity Measures for Writing

Abstract In the past two decades, syntactic complexity measures (e.g. the length or number of words per clause/t-unit/sentences and number of clauses per t-unit/sentence, and types of clauses used) have been widely used to determine and benchmark language proficiency development in speaking and writing. (Norris and Ortega 2009; Lu 2011). However, the results of some recent studies (e.g. Lu 2011; Bulté and Housen 2014; Crossley and McNamara 2014) have raised questions about the earlier findings regarding the use of such complexity measures in assessing L2 writing. While a couple of plausible explanations have been proposed for the conflicting findings, they have failed to look at the syntactic measures themselves as likely sources causing the discrepancies in the research findings. In this forum piece, we would like to argue, with empirical evidence, that the conflicting research results might have resulted from issues with some of the existing measurements of clausal and phrasal sophistication, including inconsistency and lack of necessary fine-grained differentiation in the measurements of subordination sophistication and possible inappropriate use of high values of phrasal sophistication.
Efficient Homomorphic Conversion Between (Ring) LWE Ciphertexts

In the past few years, significant progress on homomorphic encryption (HE) has been made toward both theory and practice. The most promising HE schemes are based on the hardness of the Learning With Errors (LWE) problem or its ring variant (RLWE). In this work, we present new conversion algorithms that switch between different (R)LWE-based HE schemes to take advantage of them. Specifically, we present and combine three ideas to improve the key-switching procedure between LWE ciphertexts, transformation from LWE to RLWE, as well as packing of multiple LWE ciphertexts in a single RLWE encryption. Finally, we demonstrate an application of building a secure channel between a client and a cloud server with lightweight encryption, low communication cost, and capability of homomorphic computation.
EKSTRAKSI DATA PRODUK E-MARKETPLACE SEBAGAI STRATEGI PENGOLAHAN SEGMENTASI PASAR MENGGUNAKAN WEB CRAWLER

ABSTRCTMarket Situation is something that will provide valuable benefits to increase the productivity of selling a product both conventionally and online, Indonesian e-commerce map data in the second quarter shows that the increase in sales of goods and services online is increasing, it was seen from 3 (three) major e-marketplaces namely Tokopedia, Shopee, and Bukalapak, then e-commerce activities in Indonesia in January 2019 for purchasing products or service online by 86%, it means that 86% of activities will buy products and services which located in Indonesian e-commerce.This situation creates fierce competition for sellers or business people or entrepreneurs who are just starting their business.One way for sellers to get a primacy in this competition is to apply segmentation to the products that they will sell in the online shop, but the data from a study by the association revealed that if a product stays online for a long time (more than 550 days) then 78% is very likely not to be purchased, the product placement activity in the online shop must be segmented appropriately, this research provided an effort to apply data extraction technology with web crawlers to present the segmentation, and the results of prototype testing to 9 entrepreneurs, 210 students who will start an online business, and 19 private employees which produce web crawler technology testing to help product segmentation with the result of successful was 79% with the Good category in laying online product was segmented.
Evaluating Resilience of Grid Load Predictions under Stealthy Adversarial Attacks

Recent advances in machine learning enable wider applications of prediction models in cyber-physical systems. Smart grids are increasingly using distributed sensor settings for distributed sensor fusion and information processing. Load forecasting systems use these sensors to predict future loads to incorporate into dynamic pricing of power and grid maintenance. However, these inference predictors are highly complex and thus vulnerable to adversarial attacks. Moreover, the adversarial attacks are synthetic norm-bounded modifications to a limited number of sensors that can greatly affect the accuracy of the overall predictor. It can be much cheaper and effective to incorporate elements of security and resilience at the earliest stages of design. In this paper, we demonstrate how to analyze the security and resilience of learning-based prediction models in power distribution networks by utilizing a domain-specific deep-learning and testing framework. This framework is developed using DeepForge and enables rapid design and analysis of attack scenarios against distributed smart meters in a power distribution network. It runs the attack simulations in the cloud backend. In addition to the predictor model, we have integrated an anomaly detector to detect adversarial attacks targeting the predictor. We formulate the stealthy adversarial attacks as an optimization problem to maximize prediction loss while minimizing the required perturbations. Under the worst-case setting, where the attacker has full knowledge of both the predictor and the detector, an iterative attack method has been developed to solve for the adversarial perturbation. We demonstrate the framework capabilities using a GridLAB-D based power distribution network model and show how stealthy adversarial attacks can affect smart grid prediction systems even with a partial control of network.
Memetic niching-based evolutionary algorithms for solving nonlinear equation system

In numerical computation, finding multiple roots of nonlinear equation systems (NESs) in a single run is a fundamental and difficult problem. Recently, evolutionary algorithms (EAs) have been applied to solve NESs. However, due to the diversity preservation mechanism that EAs use, the accuracy of the roots may be reduced. To remedy this drawback, we propose a generic framework of memetic niching-based EA, referred to as MENI-EA. The main features of the framework are: i) the numerical method for a NES is integrated into an EA to obtain highly accurate roots; ii) the niching technique is employed to improve the diversity of the population; iii) different roots of the NESs are located simultaneously in a singe run; and iv) different numerical methods and different niching techniques can be used in the framework. To evaluate the performance of our approach, thirty NESs were chosen from the literature as the test suite. Experimental results show that the proposed approach is capable of yielding promising performance for different NESs in both the root ratio and success rate.
A mixed solution-based high agreement filtering method for class noise detection in binary classification

Classification of noisy data has been a longstanding topic in data mining and machine learning. Many scientists have proposed effective methods to detect and eliminate such data in diverse real-world datasets. In this paper, we deal with mislabeled instances in supervised learning, including majority voting filtering and consensus voting filtering. The majority voting procedure usually incorrectly identifies many correct instances as noisy, whereas the consensus voting procedure is not able to detect at all many noisy instances. Our new method minimizes the majority and consensus filtering weaknesses by providing a novel class noise detection strategy, namely a high agreement voting filtering with mixed strategy, which proceeds by removing strong and semi-strong noisy records from the dataset as well as by relabeling weak noisy data. The proposed method, designed for binary classification problems, outperforms the high agreement voting filtering procedure. Extensive experiments conducted with 16 real datasets, using four noise filtering methods with two levels of class noise (10% and 15%), prove the superiority of the proposed methodology.
The Moonraker Study: An Experimental Evaluation of Host-Based Deception

Cyber deception has been discussed as providing enhanced cyber defense.This human subjects research, one of the first rigorously controlled studies on this topic, found that host-based deception was effective at preventing completion of a specific exfiltration task against a virtual network.In addition to impeding progress and preventing success, the deception resulted in increased confusion and surprise in the participants.This study provided the necessary rigor to scientifically attest to the effectiveness of cyber deception for cyber defense with computer specialists.
A Construction of Optimal Frequency Hopping Sequence Set via Combination of Multiplicative and Additive Groups of Finite Fields

In literatures, there are various constructions of frequency hopping sequence (FHS for short) sets with good Hamming correlations. Some papers employed only multiplicative groups of finite fields to construct FHS sets, while other papers implicitly used only additive groups of finite fields for construction of FHS sets. In this paper, we make use of both multiplicative and additive groups of finite fields simultaneously to present a construction of optimal FHS sets. The construction provides a new family of optimal (q <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sup> - 1, q <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m-t</sup> -1/r , rq <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</sup> ; q <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m-t</sup> -1/r + 1) frequency hopping sequence sets archiving the Peng-Fan bound. Thus, some FHS sets constructed in literatures using either multiplicative groups or additive groups of finite fields are included in our family. In addition, some other FHS sets can be obtained via the well-known recursive construction through one-coincidence sequence set.
A Refined Analysis of Zcash Anonymity

With the continuous development and popularity of blockchain technology, anonymity of cryptocurrency has attracted wide attention.Zcash is an altcoin of Bitcoin aiming to protect blockchain anonymity.Its anonymity is highly guaranteed by zero-knowledge proofs.However, it is still practicable to decrease Zcash's anonymity.In this paper, we provide a refined empirical analysis of Zcash anonymity.We improve current address clustering methods and increase the clustering rate by 9%.We also analyze the whole process of distributing mining reward and identify 87.5% addresses and 25.7% transactions.Besides, we simplify Zcash transaction network and then pick out nodes (edges) which play important roles in network connectivity.We show that these nodes are mostly mining pools.In particular, users participating in shieldedpool are mostly founders, miners and mining pools, although shieldedpool itself is designed for protecting anonymity of users with high privacy requirements.Our results, to an extent, are opposite to the original intention of Zcash.
SAZD: A Low Computational Load Coded Distributed Computing Framework for IoT Systems

Coded distributed computing (CDC) can overcome the problem that the computation of matrix multiplication with an extremely huge dimension cannot be executed in a single Internet-of-Things (IoT) node. All the encoding of existing CDC schemes are based on the linear combination (LC) to generate independent computation tasks, which introduces a heavy computational load, including a significant volume of expensive multiplications (compared with inexpensive additions) and even more expensive divisions to the encoding and decoding phases. Note that the number of elementwise multiplications of the LC operation during the encoding phase is N times that of the original computation task, where N denotes the number of worker nodes. In this article, to avoid expensive multiplications introduced by LC, a fresh new CDC framework based on shift-and-addition (SA) over the real field is proposed. In addition, to avoid the expensive matrix inverse operation (divisions) in the decoding phase, zigzag decoding (ZD) is incorporated. The proposed scheme, which combines SA and ZD and is hence named SAZD-based CDC, avoids expensive multiplications and divisions in both the encoding and decoding phases. It targets the following simultaneous objectives: an arbitrary K out of N generated computation tasks is independent and can recover the original computation tasks with the ZD algorithm, and the shift distance is small so as to cause a light additional computational load in the computation phase. Both analysis and practical study show that compared to the LC-based CDC, the SAZD-based CDC significantly reduces the computational load.
FPGA-Accelerated Samplesort for Large Data Sets

Sorting is a fundamental operation in many applications such as databases, search, and social networks. Although FPGAs have been shown very effective at sorting data sizes that fit on chip, systems that sort larger data sets by shuffling data on and off chip are bottlenecked by costly merge operations or data transfer time. We propose a new technique for sorting large data sets, which uses a variant of the samplesort algorithm on a server with a PCIe-connected FPGA. Samplesort avoids merging by randomly sampling values to determine how to partition data into non-overlapping buckets that can be independently sorted. The key to our design is a novel parallel multi-stage hardware partitioner, which is a scalable high-throughput solution that greatly accelerates the samplesort partitioning step. Using samplesort for FPGA-accelerated sorting provides several advantages over mergesort, while also presenting a number of new challenges that we address with cooperation between the FPGA and the software running on the host CPU. We prototype our design using Amazon Web Services FPGA instances, which pair a Xilinx Virtex UltraScale+ FPGA with a high-performance server. Our experiments demonstrate that our prototype system sorts 2^30 key-value records with a speed of 7.2 GB/s, limited only by the on-board DRAM capacity and available PCIe bandwidth. When sorting 2^30 records, our system exhibits a 37.4x speedup over the widely used GNU parallel sort on an 8-thread state-of-the-art CPU.
Investigating the Role of Formative Mobile Based Assessment in Vocabulary Learning of Pre-Intermediate EFL Learners in Comparison with Paper Based Assessment

Throughout this study technology and especially mobile phones was utilized in EFL classrooms in order to see whether it can influence the process of vocabulary formative assessment and consequently improve vocabulary learning of Iranian pre-intermediate EFL learners or not. Two groups of pre-intermediate EFL learners participated in this study. Regarding the first group (FMA) the vocabulary learning of learners was assessed formatively during ten sessions using Socrative mobile application. The vocabulary learning of the second group (FPA) was also assessed formatively but paper and pen were the instruments used by instructor and students to take the tests. After applying quasi-experimental research design including pretest, treatment and posttest and after running independent sample t tests to posttest scores, the results showed that those pre-intermediate EFL learners attending in the group where vocabulary gain was assessed formatively using mobile application named Socrative performed significantly better in posttest than group assessed formatively based on paper and pen (Sig=0.03&amp;lt;0.05). The analysis of attitude questionnaire distributed among participants of the group assessed formatively using mobile phone exhibited that they possessed a positive attitude towards mobile based testing.
BLAG: Improving the Accuracy of Blacklists

IP address blacklists are a useful source of information about repeat attackers.Such information can be used to prioritize which traffic to divert for deeper inspection (e.g., repeat offender traffic), or which traffic to serve first (e.g., traffic from sources that are not blacklisted).But blacklists also suffer from overspecialization -each list is geared towards a specific purpose -and they may be inaccurate due to misclassification or stale information.We propose BLAG, a system that evaluates and aggregates multiple blacklists feeds, producing a more useful, accurate and timely master blacklist, tailored to the specific customer network.BLAG uses a sample of the legitimate sources of the customer network's inbound traffic to evaluate the accuracy of each blacklist over regions of address space.It then leverages recommendation systems to select the most accurate information to aggregate into its master blacklist.Finally, BLAG identifies portions of the master blacklist that can be expanded into larger address regions (e.g./24 prefixes) to uncover more malicious addresses with minimum collateral damage.Our evaluation of 157 blacklists of various attack types and three ground-truth datasets shows that BLAG achieves high specificity up to 99%, improves recall by up to 114 times compared to competing approaches, and detects attacks up to 13.7 days faster, which makes it a promising approach for blacklist generation.
ProtectIOn: Root-of-Trust for IO in Compromised Platforms

Security and safety-critical remote applications such as e-voting, online banking, industrial control systems and medical devices rely upon user interaction that is typically performed through web applications.Trusted path to such remote systems is critical in the presence of an attacker that controls the user's computer.Such an attacker can observe and modify any IO data without being detected by the user or the server.We investigate the security of previous research proposals and observe several drawbacks that make them vulnerable.Based on these observations we identify novel requirements for secure IO operation in the presence of a compromised host.As a solution, we propose PROTECTION, a system that ensures IO integrity using a trusted low-TCB device that sits between the attacker-controlled host and the IO devices.PRO-TECTION intercepts the display signal and user inputs from the keyboard and mouse, and overlays secure UI on top of the HDMI frames generated by the untrusted host.The guiding design principles of PROTECTION are: (i) integrity of user input and output cannot be considered separately, (ii) all user input modalities need to be protected simultaneously, and (iii) integrity protection should not rely on error prone user tasks like checking the presence of security indicators.By following these guidelines, PROTECTION achieves strong protection for IO integrity.We also propose an extension of PROTECTION for IO confidentiality, implement a plug-and-play prototype, and evaluate its performance.
A Parallel Deep Neural Network Using Reviews and Item Metadata for Cross-Domain Recommendation

Cross-domain recommendation is an effective technique to alleviate the data sparsity problem in recommender systems by utilizing the information from relevant domains.In this paper, we propose Crossdomain Deep Neural Network (CD-DNN) for the cross-domain recommendation.CD-DNN solves the rating prediction problem by modeling users and items using reviews and item metadata, which jointly learns features of users and items from not only the target domain but also other source domains.Latent factors for users and items are learned by several parallel neural networks, and the relevance of user features and item features is learned by maximizing prediction accuracy.CD-DNN builds a single mapping for user features in the latent space, so that the network for user is optimized together with item features from other domains.Experimental results indicate that the proposed CD-DNN significantly outperforms other state-of-the-art recommendation approaches on four public datasets of Amazon and it alleviates the data sparsity problem by leveraging more data across domains.
DLchain: A Covert Channel over Blockchain Based on Dynamic Labels

With the continuous improvement of the traffic analysis techniques, traditional network covert channels based on the TCP/IP architecture are facing many risks. Blockchain is a new kind of decentralized public network. The openness together with strong tamper resistance of the blockchain makes it become a natural platform to construct covert channel. However, the existing blockchain-based covert channels have a major drawback: the recipient identifies the transactions containing covert messages through the fixed labels, which significantly reduces the availability and concealment of the system. In this paper, we propose a new blockchain covert channel construction scheme, DLchain, which substitutes the fixed labels with dynamic labels. We design a dynamic label generation algorithm based on the statistical distribution of the real transaction data to ensure the concealment of the dynamic labels. We prove that DLchain has the features of undetectability, anti-traceability and strong robustness.