Miracle-dz/UniFAR-FTUs huggingface dataset:
BIPie: Fast Selection and Aggregation on Encoded Data using Operator Specialization

Advances in modern hardware, such as increases in the size of main memory available on computers, have made it possible to analyze data at a much higher rate than before. In this paper, we demonstrate that there is tremendous room for improvement in the processing of analytical queries on modern commodity hardware. We introduce BIPie, an engine for query processing implementing highly efficient decoding, selection, and aggregation for analytical queries executing on a columnar storage engine in MemSQL. We demonstrate that these operations are interdependent, and must be fused and considered together to achieve very high performance. We propose and compare multiple strategies for decoding, selection and aggregation (with GROUP BY), all of which are designed to take advantage of modern CPU architectures, including SIMD. We implemented these approaches in MemSQL, a high performance hybrid transaction and analytical processing database designed for commodity hardware. We thoroughly evaluate the performance of the approach across a range of parameters, and demonstrate a two to four times speedup over previously published TPC-H Query 1 performance.
Toward Complex Search for Encrypted Cloud Data via Blind Index Storage

With the rapid development of cloud storage systems in recent years, end users generally outsource their data to external cloud servers for better scalability and usability. These outsourced data generally need to be encrypted before uploading due to privacy concerns of data owners. This creates a critical challenge on the accuracy of searches over the encrypted cloud data. In this paper, we develop a searchable symmetric encryption scheme for any arbitrary multi-dimensional keyword query with range query over encrypted data such as the electronic medical record data in the multi-owner multi-user environment. Within this framework, we leverage an efficient index to enrich the search conditions, and adopt an index blind storage scheme called IBS to enhance the security of the search process. Security analysis demonstrates that the proposed framework can achieve confidentiality of documents and index, trapdoor privacy, identity privacy, and the concealing of access patterns. Experimental results demonstrate that the proposed scheme can achieve excellent scalability and improved search efficiency compared to existing schemes facing similar search requests.
An Efficient Hardware Prefetcher Exploiting the Prefetch Potential of Long-Stride Access Pattern on Virtual Address

Large scale computing and applications with large data sets often cause high cache miss rate because of using array of structures or linked-list data structure. When traversing these data structures, the memory accesses may have constant long-strides across pages on virtual address (VA), but mostly scatter over the physical address (PA). Therefore, conventional stride prefetcher (SP) based on PA cannot prefetch data efficiently here. In this paper, we propose a hardware data prefetching design named Virtual Address-based Stride Prefetcher (VASP) to exploit the prefetch potential of long-stride access pattern on VA. VASP detects the access strides on VA including those cross pages, then it predicts a new VA and prefetches data after address translation. We implement VASP in the gem5 simulator and use SPEC CPU2006 integer benchmarks to evaluate its performance. Our simulation results show that, compared with SP, applying VASP to caches offers up to 43% performance improvement in the mcf benchmark, and improves the overall performance by 6%.
Numerals and their modifiers : How morphology constrains alternatives

Bare numerals (three), comparative-modified numerals (more/less than three), and superlativemodified numerals (at most/least three) differ in crucial ways with respect to bounding entailments, scalar implicatures, ignorance implicatures, and acceptability in downward-entailing environments. Many important strides have been made in deriving various subsets of their patterns, yet we still lack a unified account. In this paper I show that the key to such an account lies with the proper understanding of the contribution of the morphological pieces of these items – the numeral, much/little, and the comparative/superlative morpheme. From this we can not only obtain truth conditions that straightforwardly capture just the right bounding entailments for each item, but we can also naturally derive scalar and domain alternatives that yield just the desired scalar implicature, ignorance implicature, and acceptability in downward-entailing environments patterns. The account naturally shares features with existing alternative-based accounts of numerals, especially that of Spector (2015), but improves both empirically and conceptually on them all.
Provable Fast Greedy Compressive Summarization with Any Monotone Submodular Function

Submodular maximization with the greedy algorithm has been studied as an effective approach to extractive summarization. This approach is known to have three advantages: its applicability to many useful submodular objective functions, the efficiency of the greedy algorithm, and the provable performance guarantee. However, when it comes to compressive summarization, we are currently missing a counterpart of the extractive method based on submodularity. In this paper, we propose a fast greedy method for compressive summarization. Our method is applicable to any monotone submodular objective function, including many functions well-suited for document summarization. We provide an approximation guarantee of our greedy algorithm. Experiments show that our method is about 100 to 400 times faster than an existing method based on integer-linear-programming (ILP) formulations and that our method empirically achieves more than 95%-approximation.
A Human Activity Recognition Method using the Maximum Optical Flow based Feature Bounding Box

Extracting effective action information from the video sequence is an important process in the recognition of human activity, and it is also a challenge to ensure that the feature extraction algorithm has good time performance and high accuracy. In order to improve the validity of feature extraction and improve the time performance and accuracy of human activity recognition, a concept named maximum bounding box and an optical flow based algorithm for feature extraction are proposed. The video is preprocessed by optical flow algorithm to get the maximum bounding box. The optical flow data included in the maximum bounding box whose position can be dynamically adaptable is regarded as the characteristic value of each frame of the video sequence. After cleansing these data, the characteristic values of a fixed number of frames are fused to get the characteristics of the entire activity. Then the classification process is carried out by the support vector machine (SVM) based on Gaussian Radial Basis Function kernel (RBF kernel). The experimental results on several datasets show that, in static background, the method has good time performance and high accuracy no matter in single action recognition or in human-human interactions recognition problem.
HIPE: An Energy-Status-Data Set from Industrial Production

Energy-related data sets from industrial production are rare, and related research questions have only attracted little attention. To facilitate research on these challenges, we describe a comprehensive machine-level energy-data set from a production site and make it publicly available. We then sketch applications where our data set may serve as a benchmark and catalyst for further research.
Fault Feature Selection and Diagnosis of Rolling Bearings Based on EEMD and Optimized Elman_AdaBoost Algorithm

To realize accurate fault diagnosis of rolling bearing under random noise, a novel fault diagnosis method based on ensemble empirical mode decomposition (EEMD) and optimized Elman_AdaBoost is proposed in this paper. First, the EEMD method is used to decompose the original vibration signal into several intrinsic mode functions (IMFs). Then, the correlation coefficient and kurtosis of IMFs are used to remove any excess ingredients, and the signal is reconstructed by using the rest of IMFs. In addition, based on the analysis of the time–frequency characteristics of the reconstructed signal, the root-mean-square value and the power spectrum center are extracted as a 2-D feature vector from each reconstructed signal and regarded as the input characteristic vector of the Elman neural network. Combined with the AdaBoost classification algorithm, the Elman_AdaBoost algorithm is improved and proposed to establish the strong classifier. Finally, based on the vibration data recorded in the bearing center of the Case Western Reserve University, three kinds of bearings with different faults and normal bearings are selected as the sample data and the training samples and test samples are constructed. Experimental results show that the proposed EEMD method and optimized Elman_AdaBoost model can effectively diagnose the rolling bearing under random noise, and has the advantages of fast speed, small error, and stable performance. Comparing with the traditional Elman_AdaBoost and single Elman neural network algorithm, this paper with fewer characteristics can get better accuracy and real-time processing performance, and presents a simple and practical resolution for the fault diagnosis of rolling bearings.
Manchester Metropolitan at SemEval-2018 Task 2: Random Forest with an Ensemble of Features for Predicting Emoji in Tweets

We present our submission to the Semeval 2018 task on emoji prediction. We used a random forest, with an ensemble of bag-of-words, sentiment and psycholinguistic features. Although we performed well on the trial dataset (attaining a macro f-score of 63.185 for English and 81.381 for Spanish), our approach did not perform as well on the test data. We describe our features and classi cation protocol, as well as initial experiments, concluding with a discussion of the discrepancy between our trial and test results.
Analysis, calculation and reduction of shaft voltage in induction generators

This paper presents analysis of shaft voltage in different configurations of a Doubly Fed Induction Generator (DFIG) and an Induction Generator (IG) with a back-to-back inverter in wind turbine applications. Detailed high frequency model of the proposed systems have been developed based on existing capacitive couplings in IG & DFIG structures and common mode voltage sources. In this research work, several arrangements of DFIG based wind energy conversion systems are investigated on shaft voltage calculation and its mitigation techniques. Placements of an LC line filter in different locations and its effects on shaft voltage elimination are studied with mathematical analysis and simulations.
Catching Numeric Inconsistencies in Graphs

Numeric inconsistencies are common in real-life knowledge bases and social networks. To catch such errors, we propose to extend graph functional dependencies with linear arithmetic expressions and comparison predicates, referred to as NGDs. We study fundamental problems for NGDs. We show that their satisfiability, implication and validation problems are Σ 2 p-complete, ¶II2 p-complete and coNP-complete, respectively. However, if we allow non-linear arithmetic expressions, even of degree at most 2, the satisfiability and implication problems become undecidable. In other words, NGDs strike a balance between expressivity and complexity. To make practical use of NGDs, we develop an incremental algorithm IncDect to detect errors in a graph G using NGDs, in response to updates Δ G to G. We show that the incremental validation problem is coNP-complete. Nonetheless, algorithm IncDect is localizable, i.e., its cost is determined by small neighbors of nodes in Δ G instead of the entire G. Moreover, we parallelize IncDect such that it guarantees to reduce running time with the increase of processors. Using real-life and synthetic graphs, we experimentally verify the scalability and efficiency of the algorithms.
Taxonomic and Functional Responses of Soil Microbial Communities to Annual Removal of Aboveground Plant Biomass

Clipping, removal of aboveground plant biomass, is an important issue in grassland ecology. However, few studies have focused on the effect of clipping on belowground microbial communities. Using integrated metagenomic technologies, we examined the taxonomic and functional responses of soil microbial communities to annual clipping (2010–2014) in a grassland ecosystem of the Great Plains of North America. Our results indicated that clipping significantly (P < 0.05) increased root and microbial respiration rates. Annual temporal variation within the microbial communities was much greater than the significant changes introduced by clipping, but cumulative effects of clipping were still observed in the long-term scale. The abundances of some bacterial and fungal lineages including Actinobacteria and Bacteroidetes were significantly (P < 0.05) changed by clipping. Clipping significantly (P < 0.05) increased the abundances of labile carbon (C) degrading genes. More importantly, the abundances of recalcitrant C degrading genes were consistently and significantly (P < 0.05) increased by clipping in the last 2 years, which could accelerate recalcitrant C degradation and weaken long-term soil carbon stability. Furthermore, genes involved in nutrient-cycling processes including nitrogen cycling and phosphorus utilization were also significantly increased by clipping. The shifts of microbial communities were significantly correlated with soil respiration and plant productivity. Intriguingly, clipping effects on microbial function may be highly regulated by precipitation at the interannual scale. Altogether, our results illustrated the potential of soil microbial communities for increased soil organic matter decomposition under clipping land-use practices.
Framelet denoising for low-dose CT using deep learning

Recently, deep leaning algorithms have been extensively studied for low-dose X-ray computed tomography (CT) thanks to its superior performance with very little run-time computational complexity. Here, inspired by the recent interpretation of the deep convolutional neural network (CNN) as deep convolutional framelets, we provide a novel iterative framelet-based denoising algorithm which synergistically combines the expressive power of deep learning and the performance guarantee from the framelet-based denoising algorithms. Numerical results show that the proposed deep convolutional framelet denoising algorithm provides superior reconstruction performance.
New fully automatic method for CR image composition by white-band detection and consistency rechecking

This paper proposes a novel, fully automatic method for composing mosaic images in Computed Radiography. The method combines the detection of white-band edges with a cross- correlation technique. The white-band edges are positions of overlap lines. Several new kinds of measurements are proposed to evaluate the likelihood of a position to be a white-band edge. Multiple checks are used to reject less likely candidates. An error measure is defined for picking up the most likely candidates. The white-band candidate positions are fed to a cross-correlation method to compute the final alignment parameters for mosaic composition.
High-efficiency planar-structure perovskite solar cells via homemade chamber with low pressure and low temperature process

The sequential deposition process is widely used to fabricate planar structure perovskite solar cells because of better uniformity and perfect surface coverage of perovskite films. However, it is difficult to control the crystallization of perovskite finely. Here we improve the sequential deposition process by using a low-pressure proximity evaporation technique(LPPET). The slow grain growth rate of perovskite (CH3NH3PbI3) would make large crystallization and high quality of perovskite film. Moreover, we manipulate an innovative double-side interdiffusion process, that is, sequentially deposit CH3NH3I/PM2/CH3NH3I to fabricate the perovskite layer. In this way, PbI2 can be effectively converted into perovskite from a double side and also achieve good control of CH3NH3I deposition on the surface. The devices reach efficiency over 14%, which is the first time for TiO2 free cells fabricated by a low-pressure approach.
Sentiment Analysis Model on Weather Related Tweets with Deep Neural Network

Weather related tweets are user's comments about daily weather. We can gain useful information about how weather influence people's mood by analyzing them. This is what we called opinion mining in natural language processing field. Traditional opinion mining algorithm use feature engineering to build sentence model, and classifier like naive bayes is used for further classification. However, these feature vectors can sometimes be insufficient to represent the text, and they are manually designed, highly relevant to the problem's background. In this work1, we propose a method modeling text based on deep learning approach, which can automatically extract text feature. As for word's vector representation, we incorporate linguistic knowledge into word's representation, and use three different word representations in our model. The performance of the sentiment analysis system shows that our method is an efficient way analyzing user's sentiment on weather events.
Global citation recommendation using knowledge graphs

Scholarly search engines, reference management tools, and academic social networks enable modern researchers to organize their scientific libraries. Moreover, they often provide recommendations for scientific publications that might be of interest to researchers. Because of the exponentially increasing volume of publications, effective citation recommendation is of great importance to researchers, as it reduces the time and effort spent on retrieving, understanding, and selecting research papers. In this context, we address the problem of citation recommendation, i.e., the task of recommending citations for a new paper. Current research investigates this task in different settings, including cases where rich user metadata is available (e.g., user profile, publications, citations). This work focus on a setting where the user provides only the abstract of a new paper as input. Our proposed approach is to expand the semantic features of the given abstract using knowledge graphs – and, combine them with other features (e.g., indegree, recency) to fit a learning to rank model. This model is used to generate the citation recommendations. By evaluating on real data, we show that the expanded semantic features lead to improving the quality of the recommendations measured by nDCG@10.
An Ecosystem and IoT Device Architecture for Building Trust in the Industrial Data Space

The most recent and prominent advances in industrial computing include the growing interconnectivity of cyber-physical devices, as well as the increasing variety of complex applications exchanging data across company domains. In this context, the data becomes a valuable business asset and a trade good. The Industrial Data Space is a platform designed for the industry, allowing organizations the efficient data exchange and trade. The possibilities such platforms enable inevitably come along with new security risks regarding the establishment of trust, communication security, data usage control, or the integrity of participating systems. We define the key security requirements for the operation of such platforms in untrusted environments and present an overall security architecture for the whole ecosystem including the secure design and implementation of an architecture for the participating cyber-physical devices. On these devices, we allow for the controlled and isolated execution of services for application-specific gathering, processing and exchanging of data between organizations.
The Qualitative

Abstract This study was undertaken to determine whether an evaluation model employing multiple methods of data collection and analysis might yield more useful information for improving lifelong learning courses than existing models. Major findings included: (1) learning satisfaction appears to be dependent on the instructional environment adults may be most comfortable with and; (2) the confidence gained in using computers, rather than skills acquisition, was the greatest benefit students derived from their participation. Findings from this study suggest the value of mixed methods evaluation designs for generating information that is useful for improving lifelong learning courses. Findings also suggest the need for much more research in this domain of inquiry.
UWB at SemEval-2018 Task 10: Capturing Discriminative Attributes from Word Distributions

We present our UWB system for the task of capturing discriminative attributes at SemEval 2018. Given two words and an attribute, the system decides, whether this attribute is discriminative between the words or not. Assuming Distributional Hypothesis, i.e., a word meaning is related to the distribution across contexts, we introduce several approaches to compare word contextual information. We experiment with state-of-the-art semantic spaces and with simple co-occurrence statistics. We show the word distribution in the corpus has potential for detecting discriminative attributes. Our system achieves F1 score 72.1% and is ranked #4 among 26 submitted systems.
Learning representations through ensemble of fuzzy c-means for identification of retinal pathologies

Retinal image analysis is employed to automate screening process through low-level feature extraction and classification. Supervised classification approaches are dependent on kernels or distance metrics to handle complex manifolds as they warp feature space for effective classification with less complex boundaries between classes. Proposed approach identifies control points (Voronoi diagram) by exploring the structures of class specific manifolds which constructs complex boundaries with piecewise linear nature. Such a framework has less number of hyperparameters to tweak resulting easy control and understanding of the system. The learning characteristics of the proposed algorithm has been depicted on toy and optical coherence tomography data set. It has illustrated effective performance in identification of retinal pathologies and compared against off-the-shelf classifiers with various parameters. Proposed algorithm is capable of accommodating unsupervised approaches other than Fuzzy C-Means reflecting its adaptability.
Detecting Denial-of-Service Attacks from Social Media Text: Applying NLP to Computer Security

This paper describes a novel application of NLP models to detect denial of service attacks using only social media as evidence. Individual networks are often slow in reporting attacks, so a detection system from public data could better assist a response to a broad attack across multiple services. We explore NLP methods to use social media as an indirect measure of network service status. We describe two learning frameworks for this task: a feed-forward neural network and a partially labeled LDA model. Both models outperform previous work by significant margins (20% F1 score). We further show that the topic-based model enables the first fine-grained analysis of how the public reacts to ongoing network attacks, discovering multiple “stages” of observation. This is the first model that both detects network attacks (with best performance) and provides an analysis of when and how the public interprets service outages. We describe the models, present experiments on the largest twitter DDoS corpus to date, and conclude with an analysis of public reactions based on the learned model’s output.