Reviewerly/exHarmony huggingface dataset:
DeL-haTE: A Deep Learning Tunable Ensemble for Hate Speech Detection

Online hate speech on social media has become a fast-growing problem in recent times. Nefarious groups have developed large content delivery networks across several mainstream (Twitter and Facebook) and fringe outlets (Gab, 4chan, 8chan, etc.) to deliver cascades of hate messages directed both at individuals and communities. Thus addressing these issues has become a top priority for large-scale social media outlets. Three key challenges in automated detection and classification of hateful content are the lack of clearly labeled data, evolving vocabulary and lexicon - hashtags, emojis, etc - and the lack of baseline models for fringe outlets such as Gab. In this work, we propose a novel framework with three major contributions. (a) We engineer an ensemble of deep learning models that combines the strengths of state-of-the-art approaches, (b) we incorporate a tuning factor into this framework that leverages transfer learning to conduct automated hate speech classification on unlabeled datasets, like Gab, and (c) we develop a weak supervised learning methodology that allows our framework to train on unlabeled data. Our ensemble models achieve an 83% hate recall on the HON dataset, surpassing the performance of the state of the art deep models. We demonstrate that weak supervised training in combination with classifier tuning significantly increases model performance on unlabeled data from Gab, achieving a hate recall of 67%.
High Security for De-Duplicated Big Data Using Optimal SIMON Cipher

Cloud computing offers internet location-based affordable, scalable, and independent services. Cloud computing is a promising and a cost-effective approach that supports big data analytics and advanced applications in the event of forced business continuity events, for instance, pandemic situations. To handle massive information, clusters of servers are required to assist the equipment which enables streamlining the widespread quantity of data, with elevated velocity and modified configurations. Data deduplication model enables cloud users to efficiently manage their cloud storage space by getting rid of redundant data stored in the server. Data deduplication also saves network bandwidth. In this paper, a new cloud-based big data security technique utilizing dual encryption is proposed. The clustering model is utilized to analyze the Deduplication process hash function. Multi kernel Fuzzy C means (MKFCM) was used which helps cluster the data stored in cloud, on the basis of confidence data encryption procedure. The confidence finest data is implemented in homomorphic encryption data wherein the Optimal SIMON Cipher (OSC) technique is used. This security process involving dual encryption with the optimization model develops the productivity mechanism. In this paper, the excellence of the technique was confirmed by comparing the proposed technique with other encryption and clustering techniques. The results proved that the proposed technique achieved maximum accuracy and minimum encryption time.
Adversarial Inverse Reinforcement Learning With Self-Attention Dynamics Model

In many real-world applications where specifying a proper reward function is difficult, it is desirable to learn policies from expert demonstrations. Adversarial Inverse Reinforcement Learning (AIRL) is one of the most common approaches for learning from demonstrations. However, due to the stochastic policy, current computation graph of AIRL is no longer end-to-end differentiable like Generative Adversarial Networks (GANs), resulting in the need for high-variance gradient estimation methods and large sample size. In this work, we propose the Model-based Adversarial Inverse Reinforcement Learning (MAIRL), an end-to-end model-based policy optimization method with self-attention. By adopting the self-attention dynamics model to make the computation graph end-to-end differentiable, MAIRL has the low variance for policy optimization. We evaluate our approach thoroughly on various control tasks. The experimental results show that our approach not only learns near-optimal rewards and policies that match expert behavior but also outperforms previous inverse reinforcement learning algorithms in real robot experiments. Code is available at https://decisionforce.github.io/MAIRL/.
Square-root high-degree cubature Kalman filters for state estimation in nonlinear continuous-discrete stochastic systems

This paper addresses the problem of square-rooting in the Cubature Kalman Filtering (CKF) originated from Arasaratnam and Haykin in 2009. Presently, this technique has been accommodated to various cubature rules, including high-degree ones. Since its discovery the CKF has become one of the most powerful state estimation methods because of outstanding performance and robustness in numerous engineering applications. Its high-degree versions are shown to be accurate and even comparable to particle filters, which are considered to be among the most effective algorithms for treating nonlinear stochastic systems. However, the lack of square-root implementations within high-degree CKFs makes them vulnerable to round-off and other errors committed because of a potential covariance matrix positivity loss, which may encounter in practice. This shortcoming affects severely and fails high-degree CKFs since the Cholesky factorization of predicted and filtering covariances underlying the filters in use may not be fulfilled for indefinite matrices. Here, we resolve it by means of hyperbolic QR transforms applied for yielding J-orthogonal square roots. Our novel square-root algorithms are justified theoretically and examined and compared numerically to the existing non-square-root CKF and some other available filters in a simulated flight control scenario, including that with ill-conditioned measurements.
Parallel sentence extraction to improve cross-language information retrieval from Wikipedia

Translation language resources, such as bilingual word lists and parallel corpora, are important factors affecting the effectiveness of cross-language information retrieval (CLIR) systems. In particular, when large domain-appropriate parallel corpora are not available, developing an effective CLIR system is particularly difficult. Furthermore, creating a large parallel corpus is costly and requires considerable effort. Therefore, we here demonstrate the construction of parallel corpora from Wikipedia as well as improved query translation, wherein the queries are used for a CLIR system. To do so, we first constructed a bilingual dictionary, termed WikiDic. Then, we evaluated individual language resources and combinations of them in terms of their ability to extract parallel sentences; the combinations of our proposed WikiDic with the translation probability from the Web’s bilingual example sentence pairs and WikiDic was found to be best suited to parallel sentence extraction. Finally, to evaluate the parallel corpus generated from this best combination of language resources, we compared its performance in query translation for CLIR to that of a manually created English–Korean parallel corpus. As a result, the corpus generated by our proposed method achieved a better performance than did the manually created corpus, thus demonstrating the effectiveness of the proposed method for automatic parallel corpus extraction. Not only can the method demonstrated herein be used to inform the construction of other parallel corpora from language resources that are readily available, but also, the parallel sentence extraction method will naturally improve as Wikipedia continues to be used and its content develops.
Scaling Distributed Deep Learning Workloads beyond the Memory Capacity with KARMA

The dedicated memory of hardware accelerators can be insufficient to store all weights and/or intermediate states of large deep learning models. Although model parallelism is a viable approach to reduce the memory pressure issue, significant modification of the source code and considerations for algorithms are required. An alternative solution is to use out-of-core methods instead of, or in addition to, data parallelism. We propose a performance model based on the concurrency analysis of out-of-core training behavior, and derive a strategy that combines layer swapping and redundant recomputing. We achieve an average of 1. 52x speedup in six different models over the state-of-the-art out-of-core methods. We also introduce the first method to solve the challenging problem of out-of-core multi-node training by carefully pipelining gradient exchanges and performing the parameter updates on the host. Our data parallel out-of-core solution can outperform complex hybrid model parallelism in training large models, e.g. Megatron-LM and Turning-NLG.
A Hybrid Salp Swarm Algorithm with $$\beta $$-Hill Climbing Algorithm for Text Documents Clustering

Recently, the researchers’ attention became more interest in partitioning particular sets of documents into various subsets, due to the massive number of documents that make pattern recognition, information retrieval, and text mining more complicated. This problem is known as a text clustering problem (TCD). Several metaheuristic optimization algorithms have been adapted to address TDC optimally. A new efficient metaheuristic optimization algorithm mimics the behavior of salps in oceans known as the salp swarm algorithm (SSA) has been proposed and adapted to address different optimization problems. However, hybridizing optimization algorithms with another algorithm is becoming the focus of scholars to obtain a superior solution for the optimization problems. In this paper, a new hybrid optimization method of SSA algorithm and a well-known metaheuristic optimization algorithm called \(\beta \)-hill climbing algorithm (BHC), namely H-SSA, is proposed. The main aims of the proposed method to improve the quality of initial candidate solutions and enhance the SSA in terms of local search ability and convergence speed in attempting for optimal partitioning of the cluster. The proposed H-SSA performance is tested in the data cluster field using five standard datasets. In addition, the proposed method is tested using two scientific articles’ datasets, and six standard text datasets in the text document clustering domain. The experiment results show that the proposed method boosted the solutions in terms of convergence rate, recall, precision, F-measure, accuracy, entropy, and purity criteria. For comparative evaluation, the proposed H-SSA compared with the pure SSA algorithm and well-known clustering techniques like DBSCAN, agglomerative, spectral, k-means++ k-means clustering techniques and the optimization algorithms like KHA, PSO, GA, HS, CMAES, COA, and MVO. The comparative results prove the efficiency of the proposed method, where it exhibited and yielded better performance than the compared algorithms and techniques.
The Case for Data Visualization in the Art Classroom

In his 2011 TED Talk, Jer Thorp, the data artist-in-residence at the New York Times, presented his data visualization project portfolio and encouraged the audience to engage with data differently. ...
From Human to Robot Everyday Activity

The Everyday Activities Science and Engineering (EASE) Collaborative Research Consortium's mission to enhance the performance of cognition-enabled robots establishes its foundation in the EASE Human Activities Data Analysis Pipeline. Through collection of diverse human activity information resources, enrichment with contextually relevant annotations, and subsequent multimodal analysis of the combined data sources, the pipeline described will provide a rich resource for robot planning researchers, through incorporation in the OpenEASE cloud platform.
Analysis of Data Mining in the Group of Water Pollution Areas using the K-Means Method in Indonesia

Abstract Water is an essential requirement in human life. However, pollution causes the water quality to become poor, making it unsuitable for use. Pollution comes from rubbish and waste dumped into rivers, lakes and other water areas. This study aims to carry out a model for mapping areas contaminated with water pollution using artificial intelligence techniques. The data sample comes from the Indonesian Central Statistics Agency (abbreviated as BPS) which consists of 34 records. The data used are provinces in Indonesia that are polluted by water pollution in rural areas. The intelligence technique used is data mining using the k-means method. The variable used is the number of polluted villages by province. The mapping label used is the high cluster (K1) for water pollution and the low cluster (K2) for water pollution. Analysis using Rapid Miner software. The results showed that 4 provinces were included in the high cluster (K1) category, namely North Sumatra, West Java, Central Java, East Java. Testing of cluster results was carried out using Davies Bouldin (k = 2) with a value of 0.328, which means that the cluster results created were optimal. The results of the analysis are expected to be input for the government in focusing on areas polluted with water pollution.
Blockchain Based Auditable Access Control for Distributed Business Processes

The use of blockchain technology has been proposed to provide auditable access control for individual resources. However, when all resources are owned by a single organization, such expensive solutions may not be needed. In this work we focus on distributed applications such as business processes and distributed workflows. These applications are often composed of multiple resources/services that are subject to the security and access control policies of different organizational domains. Here, blockchains can provide an attractive decentralized solution to provide auditability. However, the underlying access control policies may be overlapping in terms of the component conditions/rules, and simply using existing solutions would result in repeated evaluation of user's authorization separately for each resource, leading to significant overhead in terms of cost and computation time over the blockchain. To address this challenge, we propose an approach that formulates a constraint optimization problem to generate an optimal composite access control policy. This policy is in compliance with all the local access control policies and minimizes the policy evaluation cost over the blockchain. The developed smart contract(s) can then be deployed to the blockchain, and used for access control enforcement. We also discuss how the access control enforcement can be audited using a game-theoretic approach to minimize cost. We have implemented the initial prototype of our approach using Ethereum as the underlying blockchain and experimentally validated the effectiveness and efficiency of our approach.
Data Management System based on Blockchain Technology for Agricultural Supply Chain

In order to better realize agricultural informatization, a data management system for agricultural supply chain, which has the characteristics of safe, credible, stable, traceable, information-sharing, and large-throughput, is needed to construct. To date, the information management method for China's agricultural supply chain is usually stored in a centralized database and file system, with weak information management capabilities, leading to problems such as theft, tampering, deletion, and inconsistencies. In light of these problems, we introduce blockchain technology which is a cutting-edge technology in digital finance and has developed rapidly. This paper proposes a data management system based on blockchain technology and affords efficient data extraction, management and access control for heterogeneous forms of data across the agricultural supply chain. The data management system includes four credible data management platforms: agricultural production information, recording transportation information, farmer-consumer transaction information, and consumer credit information. It also can add more platforms according to later needs and combine an interstellar distributed file system and smart contract technology, making it possible to conduct information security research throughout the agricultural supply chain. The proposed system effectively protects information about supply chain activities including agricultural product production, warehousing, transportation, distribution, and sales. It implements seamless connection between agricultural product production and marketing, enabling the transforming and upgrading of agriculture, thereby helping farmers increase their income and eliminate poverty.
Adaptive Physics-Based Non-Rigid Registration for Immersive Image-Guided Neuronavigation Systems

Objective: In image-guided neurosurgery, co-registered preoperative anatomical, functional, and diffusion tensor imaging can be used to facilitate a safe resection of brain tumors in eloquent areas of the brain. However, the brain deforms during surgery, particularly in the presence of tumor resection. Non-Rigid Registration (NRR) of the preoperative image data can be used to create a registered image that captures the deformation in the intraoperative image while maintaining the quality of the preoperative image. Using clinical data, this paper reports the results of a comparison of the accuracy and performance among several non-rigid registration methods for handling brain deformation. A new adaptive method that automatically removes mesh elements in the area of the resected tumor, thereby handling deformation in the presence of resection is presented. To improve the user experience, we also present a new way of using mixed reality with ultrasound, MRI, and CT. Materials and methods: This study focuses on 30 glioma surgeries performed at two different hospitals, many of which involved the resection of significant tumor volumes. An Adaptive Physics-Based Non-Rigid Registration method (A-PBNRR) registers preoperative and intraoperative MRI for each patient. The results are compared with three other readily available registration methods: a rigid registration implemented in 3D Slicer v4.4.0; a B-Spline non-rigid registration implemented in 3D Slicer v4.4.0; and PBNRR implemented in ITKv4.7.0, upon which A-PBNRR was based. Three measures were employed to facilitate a comprehensive evaluation of the registration accuracy: (i) visual assessment, (ii) a Hausdorff Distance-based metric, and (iii) a landmark-based approach using anatomical points identified by a neurosurgeon. Results: The A-PBNRR using multi-tissue mesh adaptation improved the accuracy of deformable registration by more than five times compared to rigid and traditional physics based non-rigid registration, and four times compared to B-Spline interpolation methods which are part of ITK and 3D Slicer. Performance analysis showed that A-PBNRR could be applied, on average, in <2 min, achieving desirable speed for use in a clinical setting. Conclusions: The A-PBNRR method performed significantly better than other readily available registration methods at modeling deformation in the presence of resection. Both the registration accuracy and performance proved sufficient to be of clinical value in the operating room. A-PBNRR, coupled with the mixed reality system, presents a powerful and affordable solution compared to current neuronavigation systems.
Sequential Classification With Empirically Observed Statistics

Motivated by real-world machine learning applications, we consider a statistical classification task in a sequential setting where test samples arrive sequentially. In addition, the generating distributions are unknown and only a set of empirically sampled sequences are available to a decision maker. The decision maker is tasked to classify a test sequence which is known to be generated according to either one of the distributions. In particular, for the binary case, the decision maker wishes to perform the classification task with minimum number of the test samples, so, at each step, she declares that either hypothesis 1 is true, hypothesis 2 is true, or she requests for an additional test sample. We propose a classifier and analyze the type-I and type-II error probabilities. We demonstrate the significant advantage of our sequential scheme compared to an existing non-sequential classifier proposed by Gutman. Finally, we extend our setup and results to the multi-class classification scenario and again demonstrate that the variable-length nature of the problem affords significant advantages as one can achieve the same set of exponents as Gutman's fixed-length setting but without having the rejection option.
Verifying Artificial Neural Network Classifier Performance Using Dataset Dissimilarity Measures

The specification and verification of algorithms is vital for safety-critical autonomous systems which incorporate deep learning elements. In this paper, we introduce a novel measure for quantifying the dissimilarity between the datasets used for training ANN-based image classification algorithms, and the test datasets used for verifying and evaluating classifier performance. The novel dissimilarity measure is defined in terms of each neuron's distribution of output values during training. This measure and its related variants, allow performance metrics, such as accuracy, to be placed into context by characterizing the test datasets employed for classifier evaluation. A system-level requirement could specify the permitted form of the functional relationship between classifier performance and the dissimilarity measures - placing demands on the classifier's capability to generalize to data progressively more distant from the training dataset; such a requirement can be verified by dynamic testing. Empirical results, obtained using publicly available datasets, suggest that the measures have relevance to real-world practice for both quantifying dataset dissimilarity, and specifying and verifying classifier performance.
Optimal Robot Motion Planning in Constrained Workspaces Using Reinforcement Learning

In this work, a novel solution to the optimal motion planning problem is proposed, through a continuous, deterministic and provably correct approach, with guaranteed safety and which is based on a parametrized Artificial Potential Field (APF). In particular, Reinforcement Learning (RL) is applied to adjust appropriately the parameters of the underlying potential field towards minimizing the Hamilton-Jacobi-Bellman (HJB) error. The proposed method, outperforms consistently a Rapidly-exploring Random Trees (RRT*) method and consists a fertile advancement in the optimal motion planning problem.
Quality of sentiment analysis tools

In this paper, we present a comprehensive study that evaluates six state-of-the-art sentiment analysis tools on five public datasets, based on the quality of predictive results in the presence of semantically equivalent documents, i.e., how consistent existing tools are in predicting the polarity of documents based on paraphrased text. We observe that sentiment analysis tools exhibit intra-tool inconsistency , which is the prediction of different polarity for semantically equivalent documents by the same tool, and inter-tool inconsistency , which is the prediction of different polarity for semantically equivalent documents across different tools. We introduce a heuristic to assess the data quality of an augmented dataset and a new set of metrics to evaluate tool inconsistencies. Our results indicate that tool inconsistencies is still an open problem, and they point towards promising research directions and accuracy improvements that can be obtained if such inconsistencies are resolved.