Reviewerly/exHarmony huggingface dataset:
Auto-Scaling Cloud-Based Memory-Intensive Applications

Today, Cloud providers offer simplistic scaling policies that rely on thresholds that force tenants to have a priori knowledge of their workloads. We develop a new method for scaling memory-intensive workloads that needs no thresholds. This makes it worry-free for tenants, and it adapts even as workloads evolve. This is especially hard for memory-bound applications where even a small decrease in the amount of memory available can have a dramatic, almost unbounded impact on performance. Hence, sizing a machine's physical memory correctly is critical to application performance and operating cost. To determine a natural threshold for memory-intensive applications, our approach automatically analyzes an application's miss ratio curve (MRC) and models it as a hyperbola. Intuitively, a memory scaling policy should operate at the point where the curve flattens: that is, at its intersection with its latus rectum (LR). Our system uses a new approach to constructing and analyzing MRCs at run time that captures memory references from a slice of any scalable application as it executes on standard virtual machines from any major Cloud provider. We demonstrate with multiple applications running on Amazon Web Services (AWS) and Microsoft Azure. Our implementation and evaluation show that, though the LR doesn't require tenants to set thresholds, it is effective in scaling memory-intensive workloads to save on operating costs while avoiding queuing, thrashing, or collapse. It increases throughput by 1.5× and reduces queuing delay by 2× in our evaluation.
Natural Language-to-SQL Based on Relationship Extraction

Synthesizing SQL query from a natural language with nested conditional statements is a long-standing problem. In this work, we propose a novel approach, RE-SQL, which based on relation extraction. We first set four types of relational connections are defined to represent the SQL syntax structure. Then, we propose a sequence-set model. The model receives two inputs: Natural language description containing an entity pair, table features. The output of the model includes the connection relationships of the entity pairs and the SQL semantics of the entities in the entity pair. By predicting different entities in natural language problems, we get a weighted undirected graph that is highly consistent with the SQL grammar. In the real estate information query task, our method achieves an accuracy rate of 74.35% on the nested query questions.
The effect of task-irrelevant spatial contexts on 360-degree attention

The effect of spatial contexts on attention is important for evaluating the risk of human errors and the accessibility of information in different situations. In traditional studies, this effect has been investigated using display-based and non-laboratory procedures. However, these two procedures are inadequate for measuring attention directed toward 360-degree environments and controlling exogeneous stimuli. In order to resolve these limitations, we used a virtual-reality-based procedure and investigated how spatial contexts of 360-degree environments influence attention. In the experiment, 20 students were asked to search for and report a target that was presented at any location in 360-degree virtual spaces as accurately and quickly as possible. Spatial contexts comprised a basic context (a grey and objectless space) and three specific contexts (a square grid floor, a cubic room, and an infinite floor). We found that response times for the task and eye movements were influenced by the spatial context of 360-degree surrounding spaces. In particular, although total viewing times for the contexts did not match the saliency maps, the differences in total viewing times between the basic and specific contexts did resemble the maps. These results suggest that attention comprises basic and context-dependent characteristics, and the latter are influenced by the saliency of 360-degree contexts even when the contexts are irrelevant to a task.
Metamorphic

Metamorphic is a social VR experience where participants shape their appearances and surroundings through movement and play in a series of majestically drawn worlds. This transformative encounter explores the ephemeral nature of the self, as bodies become sites of discovery and vehicles for change by offering the radical possibility of effortless transformation.
Combining Syntactic Methods With LSTM to Classify Soybean Aerial Images

Syntactic methods in computer vision represent visual patterns in a hierarchical and compositional perspective, which is converted to strings. Long short-term memory (LSTM) is able to learn patterns in sequences. In this letter, we propose a syntactic approach to represent visual patterns as sequences of symbols, and we use an LSTM as a classifier to learn the relationship between the symbols in sequences. An extensive experimental evaluation using aerial images from a soybean field captured by unmanned aerial vehicles has been conducted to compare our method with two deep learning architectures, one syntactic method, and one shallow learning algorithm. The results achieved by the proposed method maintain stability even when trained on small data sets, suggesting that representing visual patterns in a compositional way, repeating primitives, may be a viable alternative when there are only a limited number of samples for training.
Project-Based Approach in Training Business Lawyers for Digital Economy Through the Prism of Goal Setting

The goal of the study is to determine the fundamental intention of the educational project implementation in the Masters’ program for training business lawyers for the digital economy. The main methods used are the analysis method and the sociological method. The main result of the study is that the main goal of educational projects for training business lawyers is the development of a set of regulatory competences. The conclusions are that the competence-based approach is an objective trend in modern education, largely supported by students. A number of factors have been identified that impede the formation of basic lawyers’ competences for students in the digital economy, based on the sociological study. Due to the lack of reference to “digital” competences in federal state educational standards, it is advisable to introduce additional competences in the local acts of the educational organization that take into account the need for lawyers to use information and telecommunication technologies.
Fusion of Domain Knowledge and Text Features for Query Expansion in Citation Recommendation

Academic citation recommendation addresses the task of recommending citations for a scientific paper. Effective citation recommendation is greatly important for literature reviewing, literature-based discovery and a wide range of applications. In this paper, we propose a query expansion framework via fusing domain-specific knowledge and text features for academic citation recommendation. Starting from an original query, domain-specific and context-aware concepts are derived to expand the query to improve the performance of citation recommendation. From the perspective of enriching knowledge structure, domain-specific concepts are extracted from domain knowledge graphs such as ACM Computing Classification System and IEEE thesaurus. From the perspective of providing query scenarios, the query is extensively considered with context-aware concepts derived from text feature extraction. Then candidate concepts are filtered via distributed representations like BERT to expand the query. Experiments of citation recommendation for papers in public data sets show that our proposed model of query expansion improves the performance of academic citation recommendation.
Fixed point semantics for stream reasoning

Reasoning over streams of input data is an essential part of human intelligence. During the last decade stream reasoning has emerged as a research area within the AI-community with many potential applications. In fact, the increased availability of streaming data via services like Google and Facebook has raised the need for reasoning engines coping with data that changes at high rate. Recently, the rule-based formalism LARS for non-monotonic stream reasoning under the answer set semantics has been introduced. Syntactically, LARS programs are logic programs with negation incorporating operators for temporal reasoning, most notably window operators for selecting relevant time points. Unfortunately, by preselecting fixed intervals for the semantic evaluation of programs, the rigid semantics of LARS programs is not flexible enough to constructively cope with rapidly changing data dependencies. Moreover, we show that defining the answer set semantics of LARS in terms of FLP reducts leads to undesirable circular justifications similar to other ASP extensions. This paper fixes all of the aforementioned shortcomings of LARS. More precisely, we contribute to the foundations of stream reasoning by providing an operational fixed point semantics for a fully flexible variant of LARS and we show that our semantics is sound and constructive in the sense that answer sets are derivable bottom-up and free of circular justifications.
Research on Adaptive ISOMAP Algorithm and Application in Intrusion Detection

Abstract In view of the low accuracy and many redundant attributes in the detection algorithms, an intrusion detection algorithm based on adaptive Isomap is proposed. The algorithm uses the sparse representation theory to adaptively select the neighborhood of data points. Then the sparse coefficients are used as the distance weight to improve the data discrimination ability. Finally, the improved isometric feature mapping algorithm is introduced to the intrusion detection as the feature extraction module. The algorithm not only overcomes the difficulty of manual parameter adjustment, but also has strong robustness. Experimental results show that using this method to extract intrusion detection features can effectively improve the detection accuracy, and at the same time improve the detection accuracy of Probe, U2R, R2L.
A Novel Method to Prevent Misconfigurations of Industrial Automation and Control Systems

Configuration errors are among the dominant causes of system faults for the industrial automation and control systems (IACS). It is difficult to detect and correct such errors of IACS as there are various kinds of systems and devices with miscellaneous configuration specifications. In this article, we first propose a streaming algorithm to keep all the configuration changes in the limited memory space. When making a new configuration change, another novel streaming algorithm is proposed to search and return all the similar historical changes, which can be used to validate this new one. So far, we are the first to model the configuration changes of IACS as a data stream and apply the streaming similarity search in correcting configuration errors while overcoming the inherent unbounded-memory bottleneck. The theoretical correctness and complexity analyses are presented. Experiments with real and synthetic datasets confirm the theoretical analyses and demonstrate the effectiveness of the proposed method in preventing misconfigurations of IACS.
Communication-aware Job Scheduling using SLURM

Job schedulers play an important role in selecting optimal resources for the submitted jobs. However, most of the current job schedulers do not consider job-specific characteristics such as communication patterns during resource allocation. This often leads to sub-optimal node allocations. We propose three node allocation algorithms that consider the job's communication behavior to improve the performance of communication-intensive jobs. We develop our algorithms for tree-based network topologies. The proposed algorithms aim at minimizing network contention by allocating nodes on the least contended switches. We also show that allocating nodes in powers of two leads to a decrease in inter-switch communication for MPI communications, which further improves performance. We implement and evaluate our algorithms using SLURM, a widely-used and well-known job scheduler. We show that the proposed algorithms can reduce the execution times of communication-intensive jobs by 9% (326 hours) on average. The average wait time of jobs is reduced by 31% across three supercomputer job logs.
Copy-Move Forgery Detection Methods: A Critique

With the advancement of image editing tools, legitimacy and creditability of the images are put in jeopardy. There are various types of image forgeries techniques like splicing, retouching, false captioning, and copy-move. But the most predominant forgery techniques are splicing and copy-move forgery. In copy-move forgery, a part of the image is copied in the same image and in splicing another image in order to conceal or to duplicate the information residing in the image. In this critique, the copy-move forgery detection (CMFD) methods from 2013 onwards are reviewed under four categories—block-based, keypoint-based, hybrid-, and deep learning-based methods.
FAT-RE: A faster dependency-free model for relation extraction

Recent years have seen the dependency tree as effective information for relation extraction. Two problems still exist in previous methods: (1) dependency tree relies on external tools and needs to be carefully integrated with a trade-off between pruning noisy words and keeping semantic integrity; (2) dependency-based methods still have to encode sequential context as a supplement, which needs extra time. To tackle the two problems, we propose a faster dependency-free model in this paper: regarding the sentence as a fully-connected graph, we customize the vanilla transformer architecture to remove the irrelevant information via filtering mechanism and further aggregate the sentence information through the enhanced query. Our model yields comparable results on the SemEval2010 Task8 dataset and better results on the TACRED dataset, without requiring external information from the dependency tree but with improved time efficiency.
A Survey Deep Learning Based Relation Extraction

Abstract From human understanding of natural language to machine understanding of natural language, NLP has become the mainstream technology in the world.Extracting useful information from massive information, namely information extraction (IE), such as relation extraction (RE), is one of the important semantic processing tasks.With the explosion of web texts and the emergence of new relations, the margin of human knowledge has increased dramatically, and unstructured text processing has become a problem that needs to be overcome, so people have been working on RE for many years. From the earliest pattern matching to the current popular neural network, RE has made significant progress. After reviewing the development history of relation extraction, this paper analyzes and discuss the development of existing neural network models and pre-trained language models from static technology to dynamic technology and reinforcement learning; finally, combined with the latest development of NLP research technology, the future research direction and trend of entity relation extraction are prospected.
Indonesian Graphemic Syllabification Using n-Gram Tagger with State-Elimination

Syllabification can be approached using either grapheme or phoneme-based. Graphemic syllabification is simpler than phonemic syllabification since it does not require grapheme-to-phoneme conversion (G2P). Both phonemic and graphemic syllabification has been done on Indonesian words with average SER of 0.64% and 2.27%, respectively. The performance of Indonesian graphemic syllabification is considerably lower than the phonemic one. This research aims to improve Indonesian graphemic syllabification using a syllable boundary tagger based on the statistical n-gram model. Using fivefold cross-validation on 50k formal Indonesian words, the proposed model gives an average syllable error rate (SER) of 0.94% while the introduced state-elimination procedure reduces the SER to 0.92%, which is much lower than the previous Indonesian graphemic syllabification. Most syllabification errors come from derivative words and adapted foreign terms.
Chaotic Dynamically Dimensioned Search Algorithm

Dynamically dimensioned search algorithm (DDS) has been proved to be an effective algorithm for solving optimization problems in actual applications, such as distributed watershed model calibration, reservoir operation, pump-and-treat problem, and Radiation Therapy (IMRT) beam angle optimization. However, it always traps in local optimal, especially for multimodal problems. To further improve the performance of DDS, this article proposes a novel chaotic dynamically dimensioned search algorithm (CDDS) by incorporating chaos theory into DDS. The improvement is performed by using three strategies: chaotic initialization, a new Gaussian mutation operator, and a chaotic search. The chaotic map generates an initial population to improve the quality of the initial solution. Chaotic initialization can enhance the exploration ability of DDS because of its intrinsic ergodic and stochastic property. A new Gaussian mutation operator is used to increase convergence speed and the exploitation ability. Meanwhile the chaotic search is utilized to jump out of the local optimal. The optimal CDDS among 13 CDDS variants is compared with other optimization algorithms on 20 classical benchmark test functions, 14 shifted rotated benchmark functions from CEC2005, and 30 benchmark functions from CEC2014 in terms of exploitation and exploration. We testify the applicability and effectiveness on a cascade reservoirs operation optimization problem in real world tasks. The experimental results and analyses demonstrate the superiority of the proposed algorithm in increasing the solution quality and accelerating the convergence.
SCX-SD: Semi-supervised Method for Contextual Sarcasm Detection

Sarcasm detection is to identify the text with the author's sarcastic attitude. Verbal sarcasm is one main error sources of sentiment analysis tasks. However, labeled sarcastic samples are expensive to obtain. Previous approaches, e.g., model user and topic embedding from multiple perspectives together with large-scale network training, are not suitable for real business scenarios that expect low cost and high speed. In this paper, we propose a semi-supervised method for contextual sarcasm detection in online discussion forums. We adopt author and topic sarcastic prior preference as context embedding that supply simple but representative background knowledge. Then we introduce a sarcasm-unlabeled learning method to utilize a few labeled sarcastic samples and model the classification boundary. Experiments are conducted on real-world data from Reddit, and the results indicate the outperformance over existing methods.
A Construction Engineering Domain New Word Detection Method with the Combination of BiLSTM-CRF and Information Entropy

The study of new word detection is of great significance of the improvement on the performance of Chinese natural language processing tasks. To solve the problem of the inconsistency of coarse-grained long-word boundaries and the detection of compound words in detection of new words, a new word detection method with the combination of BiLSTM-CRF and information entropy(IE) is proposed. First, BiLSTM model extracts candidate new words. Then, information entropy splicing candidate new words to redefine word boundaries. The BiLSTM model could effectively utilize context information, CRF could consider the relationship between adjacent labels, realizing sentence horizontal sequence labeling, which could solve the problem that some compound words and long words are difficult to identify. The results of experiment show that our model achieves better performance on construction engineering datasets.
A CAD Tool for Breast Cancer Prediction using Naive Bayes Classifier

Even though today's medical field is technologically advanced, some diseases still haunt the human race by posing a hustle in its existence. In addition to the sophisticated tools and techniques for disease diagnosis, recent use of information and communication technology (ICT) has also tightened its spine to serve this noble cause. To have an interior view of the body without surgery, medical imaging is a predominant technique behind early/automatic diagnosing and detecting diseases. Image mammography is the primary asset assisting doctors for having a projection of the interiors of breast tissues, thus offering a crucial big stick in the diagnosis of malignancy/non- malignancy in the tissues. Using certain supervised and unsupervised filters offered by Weka on top of BCDR-F01 cancer benhcmark dataset, this work intends to increase the objectivity of clinical diagnostics. Misclassification cost of Naive Bayes algorithm is measured and compared the same with misclassification cost measured after applying respective filters. The results show the accuracies in case of supervised attribute DISCRETIZE filter, supervised instance RESAMPLE filter and unsupervised attribute PKIIDiscretize filter get amplified from 73.7569 % to 81.768 %, 85.0829 %, and 78.7293 % and only in case of unsupervised instance RESAMPLE filter, it shows a minute decrease to 73.4807 %.
Differential Acmeological Approach to The Design of Psychological Support for Professional and Personal Development of Pedagogical University Students

The article presents the results of a comparative analysis of psychological assistance models functioning in Russian universities. The study objective is to identify and substantiate the provisions of the differential acmeological approach as the basis for the design of psychological assistance for professional and personal development of pedagogical university students. The model of psychological assistance for professional and personal development of pedagogical university students is designed based on the results of comparative analysis, the descriptive method, and the method of functional and structural modeling. It is demonstrated that the creation of a unique psychological assistance system stimulating the maximum realization of the creative potential of the educational process subjects’ personalities as a part of the educational activity of a pedagogical university opens up additional opportunities for the individualization of professional training. The differential acmeological approach to the design of psychological assistance for professional and personal development of pedagogical university students presented in the article allows integrating the potential of general professional training courses in the system of psychological support and assistance in students’ self-knowledge and professional and personal development.
Construction and Application of Online and Offline Mixed Teaching Mode Under the Background of “Internet + Education”

With the development of “Internet + education”, the mixed teaching mode that integrates network platform and traditional classroom will become an important development direction. Based on the constructivism learning theory and taking Automatic control principle course as the carrier, this paper constructs the online and offline mixed three-stage teaching mode and integration of process evaluation and summative evaluation multiple evaluation system. Through student satisfaction survey and teaching effect analysis, it is shown that this teaching mode can improve students’ autonomous learning ability and teaching efficiency.