Reviewerly/exHarmony huggingface dataset:
Enabling Microarchitectural Randomization in Serialized AES Implementations to Mitigate Side Channel Susceptibility

Highly serialized implementations of the AES block cipher are used in lightweight applications where low area and low power are the primary concerns. Security of these lightweight designs becomes increasingly critical on resource-constrained devices in the Internet of Things era. The AES algorithm does not have any significant known cryptanalytic weaknesses, but keys can often be extracted by attacking implementation weaknesses using side channel information leakage or fault injection. Highly serialized AES implementations compute on individual bytes/words of data in each cycle which leaves them especially sensitive to side channel key extraction because there is less overall power consumption to obscure side channel leakages. In this work, we present an efficient AES microarchitecture that randomizes sub-round operations and reduces susceptibility to power side channel attacks. The architecture we propose is compatible with, and complementary to, all existing circuit-level side channel countermeasures. We design an 8-bit AES architecture in a commercial 16nm FinFET technology and observe an order of magnitude improvement in side channel protection at a cost of 36% more area and 25% more energy per encryption. Testchip measurement shows 0.93pJ/bit energy consumption at 10MHz.
Automatic Pronunciation Evaluation in High-states English Speaking Tests Based on Deep Neural Network Models

This paper introduces our state-of-the-art automatic pronunciation evaluation technologies for high-states English speaking tests. A noise robust implementation of Goodness of Pronunciation based automatic scoring method and novel acoustic adaptation techniques have been proposed. Experimental results show that the proposed techniques outperform conventional methods. Using these posterior based sentimental features combined with supra-segmental prosodic features, the proposed pronunciation evaluation system can provide human expert level performance for high-states English speaking tests.
ERP System Integration with Mobile Applications Using Service Oriented Architecture

A furniture rental service multinational company has a mobile application that is used by the salesperson to make sales transactions. Currently the mobile application gets data from the ERP system by importing and exporting it so that the data used is not real-time. To support the creation of real-time data communication between ERP systems and mobile applications, a replacement method for the export and import process is needed. Therefore, this study makes several web services that can be used to replace the export and import processes through the integration of Enterprise Resource Planning (ERP) with mobile applications using Service Oriented Architecture (SOA). Web services are made using RESTful and have passed several stages of testing using the QOS standard. Based on the results of testing using Jmeter with a simulation of 20 users obtained latency on the web service is 3 seconds and 4 seconds load time with an average data size of 2599 bytes so it can be concluded that this quality is quite good in terms of performance and accessibility. Test results using vooky show no bugs, but there are still shortcomings in security, which are caused by the lack of security in the header. With the results of these tests, the webservice can be used to replace the export and import process that have been used by the salesperson, so the data communication between ERP and mobile applications becomes real-time.
Learning parameters of Bayesian networks from datasets with systematically missing data: A meta–analytic approach

Previous research suggested that using additional data sources could improve parameter learning in Bayesian networks. However, when additional datasets do not include all network variables, neither standard Bayesian network learning techniques nor standard missing data methods can be applied. In such situations, the use of a meta–analytic approach is proposed. The performance of one such meta–analytic approach was evaluated by simulating several study results on two real–life biomedical examples (one discrete and one Gaussian Bayesian network). Regardless of the network type, the meta–analytic approach showed higher mean log–likelihood values, less sensitive to the presence of heterogeneity, than a single dataset analysis. The difference between the two methods was most pronounced when sample sizes were small (N=100). For the meta–analytic approach, the increase in log–likelihood was in most cases positively related to the number of nodes estimated with additional data. However, as in the case of single dataset analysis, care is needed when estimating rare event probabilities from small datasets due to the problems with unidentifiability and increased bias.
A Case for Stale Synchronous Distributed Model for Declarative Recursive Computation

Abstract A large class of traditional graph and data mining algorithms can be concisely expressed in Datalog, and other Logic-based languages, once aggregates are allowed in recursion. In fact, for most BigData algorithms, the difficult semantic issues raised by the use of non-monotonic aggregates in recursion are solved by Pre-Mappability ( ${\cal P}$ reM ), a property that assures that for a program with aggregates in recursion there is an equivalent aggregate-stratified program. In this paper we show that, by bringing together the formal abstract semantics of stratified programs with the efficient operational one of unstratified programs, $\[{\cal P}\]$ reM can also facilitate and improve their parallel execution. We prove that $\[{\cal P}\]$ reM -optimized lock-free and decomposable parallel semi-naive evaluations produce the same results as the single executor programs. Therefore, $\[{\cal P}\]$ reM can be assimilated into the data-parallel computation plans of different distributed systems, irrespective of whether these follow bulk synchronous parallel (BSP) or asynchronous computing models. In addition, we show that non-linear recursive queries can be evaluated using a hybrid stale synchronous parallel (SSP) model on distributed environments. After providing a formal correctness proof for the recursive query evaluation with $\[{\cal P}\]$ reM under this relaxed synchronization model, we present experimental evidence of its benefits.
Robust hierarchical clustering for novelty identification in sensor networks: With applications to industrial systems

The paper proposes a new, robust cluster-based classification technique for Novelty Identification in sensor networks that possess a high degree of correlation among data streams. During normal operation, a uniform cluster across objects (sensors) is generated that indicates the absence of novelties. Conversely, in presence of novelty, the associated sensor is clustered distinctly from the remaining sensors, thereby isolating the data stream which exhibits the novelty. It is shown how small perturbations (stemming from noise, for instance) can affect the performance of traditional clustering methods, and that the proposed variant exhibits a robustness to such influences. Moreover, the proposed method is compared with a recently reported technique, and shown that it performs 365% faster computationally. To provide an application case study, the technique is used to identify emerging fault modes in a sensor network on a sub-15MW industrial gas turbine in presence of other abrupt, but normal changes that visually might otherwise be interpreted as malfunctions.
Bank Cheque Validation Using Image Processing

Bank cheques, as documents issued by banks can be used as a form of bills capable of monetary exchange, allowing a payee a certain sum of money from the account of drawer. However, due to many fraudulent practices and a need of faster cheque clearance, there had been advances in the process of cheque clearance. Consequently, to aid the process of cheque validation this research work focuses on implementing image processing techniques such as OCR, ANN and Deep Learning to extract key parameters essential for cheque validation. These techniques can be used in sequential manner to automate the task of cheque validation. For extracting machine typographic information Optical Character Recognition is used. Whereas, for the handwritten characters we have used CNN trained using MNIST dataset. The accuracy achieved in handwritten character recognition is 99.14%. For testing purposes IDRBT cheque dataset is used comprising cheque leaflets of different banks.
Deep Learning of Human Information Foraging Behavior with a Search Engine

In this paper, a two-level deep learning framework is presented to model human information foraging behavior with search engines. A recurrent neural network architecture is designed using LSTM as the base unit to explicitly consider the temporal and spatial dependencies of information scents, the key concept in Information Foraging Theory. The target is to predict several major search behaviors, such as query abandonment, query reformulation, number of clicks, and information gain. The memory capability and the sequence structure of LSTM allow to naturally mimic not only what users are perceiving and performing at the moment but also what they have seen and learned from the past during the search dynamics. The promising results indicate that our information scent models with different input variations were better, compared to the state-of-the art neural click models, at predicting some search behaviors. When incorporating the knowledge from a previous query in the same search session, the prediction of current query abandonment, pagination, and information gain has been improved. Compared to the well known neural click models that model search behaviors under a single search query thread, this study takes a broader view to consider an entire search session which may contain multiple queries. More importantly, our model takes the search result relevance pattern on the Search Engine Results Pages (SERP) as a whole as the information scent input to the deep learning model, instead of considering one search result at each step. The results have insights on the impact of information scents on how people forage for information, which has implications for designing or refining a set of design guidelines for search engines.
Robust Neighborhood Covering Reduction with Determinantal Point Process sampling

Neighborhood Covering Reduction (NCR) methods are too rigid to filter out redundant neighborhoods and sensitive to noise. To tackle this problem, we propose a flexible neighborhood reduction method based on Determinantal Point Process (DPP) sampling in this paper. Sampling from a DPP, the probabilities of subset selection are computed from the correlation matrix of items and the subsets of diverse items will be assigned high probabilities. We model the process of neighborhood selection as a DPP and thereby implement a NCR algorithm with DPP neighborhood sampling (NCRDPP). NCRDPP selects significant neighborhoods of both high quality and diversity to form concise coverings as the approximations of data distributions, which facilitate the model generalization and robustness of neighborhood-based learning. Experimental results verify the superiority of DPP sampling for neighborhood selection and the robustness of NCRDPP method for noisy data classification.
Investigation on Stochastic Model Refinement for Precise Underwater Positioning

More accurate and realistic stochastic model is required for high-precision underwater positioning. The common stochastic modeling procedure, assuming that the measurements are statistically independent in space and time and have same accuracy, is certainly not realistic. Any misspecification in the stochastic model may have a significant effect on underwater acoustic data processing. Taking the heteroscedastic space and time correlation into consideration, a novel stochastic modeling procedure based on an iterative minimum norm quadratic unbiased estimator method has been developed to improve underwater positioning accuracy. Testing results by experiment data show that the positioning accuracy can be superiorly improved from 1.226, 1.434, and 1.018 m based on a traditional stochastic model estimation scheme to 0.935, 0.336, and 0.190 m for three transponders when adopting the new method.
A Novel Doubly Reweighting Multisource Transfer Learning Framework

In this paper, to effectively use the decision knowledge from multiple source domains to predict the labels of samples in the target domain, a novel doubly reweighting multisource transfer learning called DRMTL framework is proposed. DRMTL aims to simultaneously optimize the structural risk function, domain reweighting adaptation, pointwise reweighting adaptation and manifold consistency. The merits of DRMTL include the following: 1) The importance of every source domain can be evaluated using the proposed novel flexible weighting index; 2) The loss between an unknown label prediction and its prediction by some source decision function for a target sample can be reweighted using a novel domain separator; and 3) The manifold structure of the target domain is effectively used in this framework. Finally, a specific learning algorithm, i.e., a doubly reweighting multisource transfer learning using the regularized least-squares classifier called DRM-RLS, is proposed using the DRMTL framework and the classical regularized least-squares classifier, and its convergence is also proven. Our experimental results from several real-world datasets reveal that the proposed approach outperforms several state-of-the-art transfer learning algorithms.
"Invisible Work" as a Lens for Understanding Humanware's Role in Research Cloud Computing

Researchers in various scientific disciplines are leveraging cloud computing resources to enhance the scale, speed, and portability of research processes and products. Like any other cyberinfrastructure, cloud computing deployment in scientific research requires arrangements of technologies, people, organizations, and institutions to ensure smooth functioning and sustainability. To date, published discussions of cloud computing's promise have often placed technological capabilities and financial benefits front-and-center. These accounts are in line with traditional return-on-investment models for assessing a new technology's viability. The attention to ROI, however, has left assessment of the "invisible" work (and costs) placed on human actors in reaching cloud computing's promise relatively unexplored. In this paper, I report on the findings of 45 interviews with career researchers, research support staff, and student researchers engaged in cloud vendor-enabled research. The purpose of conducting the interviews was to identify commonalities in the labor required to start, maintain, or migrate research processes to the cloud via vendors (e.g., AWS, Azure, and Google Cloud). Two central types of "invisible" labor emerged as themes across the interviews: absorbing the time costs of learning new skills to migrate research to the cloud and managing billing for multiple, decentralized projects. In the discussion, I contextualize these two types of seemingly mundane work in broader debates about the burden new cloud computing technologies might place on scientists and research support staff. I conclude by suggesting that continued documentation and analysis of cloud research-enabling labor is needed to ensure that these invisible costs are understood and, perhaps in the future, shared appropriately among vendors, universities, and researchers.
Illuminant-invariant stereo matching using cost volume and confidence-based disparity refinement

In stereo-matching techniques for three-dimensional (3D) vision, illumination change is a major problem that degrades matching accuracy. When large intensity differences are observed between a pair of stereos, it is difficult to find the similarity in the matching process. In addition, inaccurately estimated disparities are obtained in textureless regions, since there are no distinguishable features in the region. To solve these problems, this paper presents a robust stereo-matching method using illuminant-invariant cost volume and confidence-based disparity refinement. In the step of matching a stereo pair, the proposed method combines two cost volumes using an invariant image and Weber local descriptor (WLD), which was originally motivated by human visual characteristics. The invariant image used in the matching step is insensitive to sudden brightness changes by shadow or light sources, and WLD reflects structural features of the invariant image with consideration of a gradual illumination change. After aggregating the cost using a guided filter, we refine the initially estimated disparity map based on the confidence map computed by the combined cost volume. Experimental results verify that the matching computation of the proposed method improves the accuracy of the disparity map under a radiometrically dynamic environment. Since the proposed disparity refinement method can also reduce the error of the initial disparity map in textureless areas, it can be applied to various 3D vision systems such as industrial robots and autonomous vehicles.
Towards an Energy Efficient Computing With Coordinated Performance-Aware Scheduling in Large Scale Data Clusters

Many prior works have investigated on how to increase the job processing performance and energy efficient computing in large scale clusters. However, they employ serialized scheduling approaches encompassed with task straggler “hunting” techniques which launches speculative tasks after detecting slow tasks. These slow tasks are detected through node instrumentation which collects system level information whilst tracking the task execution progress. Such approaches are however detrimental towards achieving maximum processing performance and preserving cluster energy as they increase communication overheads. In this paper, we observe that node instrumentation and serialized scheduling in existing works does not only degrade the job processing performance, but also increase cluster energy consumption. To alleviate this, we propose EPPADS, a light-weight scheduler which eradicates the need for instrumentation modules for job scheduling purposes. EPPADS schedules tasks in two stages, the slow-start phase (SSP) and accelerate phase (AccP). The SSP schedules initial tasks in the queue using baseline FIFO scheduling and records the initial execution times of the processing nodes, whilst tagging the effective and straggling nodes. The AccP uses the initial execution times to compute the processing nodes task distribution ratio of remaining tasks and schedules them in parallel using a single scheduling I/O, boosting up the processing performance. To amortize the computing energy costs, EPPADS implements a power management module that coordinates with the scheduling module and leverage on node tagging information, to place nodes in two different power transition pools, i.e., high and low state power pools. A single power transition signal per pool is then broadcasted to lower or raise the energy state in the low-power state pool and high-power state pool. Our evaluation using a Hadoop cluster shows that EPPADS achieves 30% and 22% performance improvement and 15% to 20% energy savings as compared to the FIFO and DynMon schedulers, respectively.
Action Spaces in Deep Reinforcement Learning to Mimic Human Input Devices

Enabling agents to generally play video games requires to implement a common action space that mimics human input devices like a gamepad. Such action spaces have to support concurrent discrete and continuous actions. To solve this problem, this work investigates three approaches to examine the application of concurrent discrete and continuous actions in Deep Reinforcement Learning (DRL). One approach implements a threshold to discretize a continuous action, while another one divides a continuous action into multiple discrete actions. The third approach creates a multiagent to combine both action kinds. These approaches are benchmarked by two novel environments. In the first environment (Shooting Birds) the goal of the agent is to accurately shoot birds by controlling a cross-hair. The second environment is a simplification of the game Beastly Rivals On-slaught, where the agent is in charge of its controlled character's survival. Throughout multiple experiments, the bucket approach is recommended, because it is trained faster than the multiagent and is more stable than the threshold approach. Due to the contributions of this paper, consecutive work can start training agents using visual observations.
Research on Denoising of Finger Vein Image Based on Deep Convolutional Neural Network

Because of finger vein collection device and using habit, the collected finger vein images contain noise, the extraction of finger vein features may cause errors. For the problem, a kind of finger vein image denoising method is proposed based on the deep convolution neural network, which is to collect the finger vein image as the input image of network, the non-linear mapping of the noise image to the denoising image is constructed through the hidden layer, a symmetric network structure is formed by the convolution subnet and the deconvolution subnet, convolution subnet learns finger vein image characteristics, the deconvolution subnet restores the original image according to the feature graph, and the modified linear unit is used to obtain more details of finger vein texture. The experiment was conducted as a training set in the finger vein database of the polytechnic university of Hong Kong, use Tensorflow model to train network model in GPU environment. The experimental results show that this method can remove the noise in the finger veins, the higher peak signal-to-noise ratio is obtained, which lays the foundation for the subsequent feature extraction.
Research on the Platform of Online Education Platform Based on Cloud Computing

Different from the development of traditional web-based learning system, Based on Cloud Computing network learning platform focuses more on how to build the system based on cloud computing infrastructure, thus enabling the system to take full advantage of cloud computing technology to provide more comprehensive, practical and efficient services. The construction of our school education information infrastructure has begun to take shape, the level of construction steadily improved, Due to significant improvement in the quantity and quality of digital educational resources, a large number of excellent information-based talents have been cultivated to contribute to universal lifelong education and the construction of study-oriented society.
A Hybrid Context Aware Recommender System with Combined Pre and Post-Filter Approach

The domain of context aware recommender approaches has made substantial advancement over the last decade, but many applications still do not include contextual information while providing recommendations. Contextual information is crucial for various application areas and should not be ignored. There are generally three algorithms which can be used to include context and those are: pre-filter approach, post-filter approach, and contextual modeling. Each of the algorithms has their own drawbacks. The proposed approach modifies the post filter approach to rectify its shortcomings and combines it with the pre-filter approach based on the importance of contextual attribute provided by the user. The results of experimental setup also demonstrate that the proposed system improves the precision and ranking of the recommendations provided to user. With the help of this hybrid approach, the proposed system eliminates the problem of sparsity which is present in the pre-filter algorithm, and has performance improvement over the traditional post-filter approach.