Reviewerly/exHarmony huggingface dataset:
Multi-Dimensional Classification via kNN Feature Augmentation

Multi-dimensional classification (MDC) deals with the problem where one instance is associated with multiple class variables, each of which specifies its class membership w.r.t. one specific class space. Existing approaches learn from MDC examples by focusing on modeling dependencies among class variables, while the potential usefulness of manipulating feature space hasn’t been investigated. In this paper, a first attempt towards feature manipulation for MDC is proposed which enriches the original feature space with kNNaugmented features. Specifically, simple counting statistics on the class membership of neighboring MDC examples are used to generate augmented feature vector. In this way, discriminative information from class space is encoded into the feature space to help train the multi-dimensional classification model. To validate the effectiveness of the proposed feature augmentation techniques, extensive experiments over eleven benchmark data sets as well as four state-of-the-art MDC approaches are conducted. Experimental results clearly show that, compared to the original feature space, classification performance of existing MDC approaches can be significantly improved by incorporating kNN-augmented features.
Deep Robust Unsupervised Multi-Modal Network

In real-world applications, data are often with multiple modalities, and many multi-modal learning approaches are proposed for integrating the information from different sources. Most of the previous multi-modal methods utilize the modal consistency to reduce the complexity of the learning problem, therefore the modal completeness needs to be guaranteed. However, due to the data collection failures, self-deficiencies, and other various reasons, multi-modal instances are often incomplete in real applications, and have the inconsistent anomalies even in the complete instances, which jointly result in the inconsistent problem. These degenerate the multi-modal feature learning performance, and will finally affect the generalization abilities in different tasks. In this paper, we propose a novel Deep Robust Unsupervised Multi-modal Network structure (DRUMN) for solving this real problem within a unified framework. The proposed DRUMN can utilize the extrinsic heterogeneous information from unlabeled data against the insufficiency caused by the incompleteness. On the other hand, the inconsistent anomaly issue is solved with an adaptive weighted estimation, rather than adjusting the complex thresholds. As DRUMN can extract the discriminative feature representations for each modality, experiments on real-world multimodal datasets successfully validate the effectiveness of our proposed method.
Fast PMI-Based Word Embedding with Efficient Use of Unobserved Patterns

Continuous word representations that can capture the semantic information in the corpus are the building blocks of many natural language processing tasks. Pre-trained word embeddings are being used for sentiment analysis, text classification, question answering and so on. In this paper, we propose a new word embedding algorithm that works on a smoothed Positive Pointwise Mutual Information (PPMI) matrix which is obtained from the word-word co-occurrence counts. One of our major contributions is to propose an objective function and an optimization framework that exploits the full capacity of “negative examples”, the unobserved or insignificant wordword co-occurrences, in order to push unrelated words away from each other which improves the distribution of words in the latent space. We also propose a kernel similarity measure for the latent space that can effectively calculate the similarities in high dimensions. Moreover, we propose an approximate alternative to our algorithm using a modified Vantage Point tree and reduce the computational complexity of the algorithm to |V |log|V | with respect to the number of words in the vocabulary. We have trained various word embedding algorithms on articles of Wikipedia with 2.1 billion tokens and show that our method outperforms the state-of-the-art in most word similarity tasks by a good margin.
Free VQA Models from Knowledge Inertia by Pairwise Inconformity Learning

In this paper, we uncover the issue of knowledge inertia in visual question answering (VQA), which commonly exists in most VQA models and forces the models to mainly rely on the question content to “guess” answer, without regard to the visual information. Such an issue not only impairs the performance of VQA models, but also greatly reduces the credibility of the answer prediction. To this end, simply highlighting the visual features in the model is undoable, since the prediction is built upon the joint modeling of two modalities and largely influenced by the data distribution. In this paper, we propose a Pairwise Inconformity Learning (PIL) to tackle the issue of knowledge inertia. In particular, PIL takes full advantage of the similar image pairs with diverse answers to an identical question provided in VQA2.0 dataset. It builds a multi-modal embedding space to project pos./neg. feature pairs, upon which word vectors of answers are modeled as anchors. By doing so, PIL strengthens the importance of visual features in prediction with a novel dynamic-margin based triplet loss that efficiently increases the semantic discrepancies between pos./neg. image pairs. To verify the proposed PIL, we plug it on a baseline VQA model as well as a set of recent VQA models, and conduct extensive experiments on two benchmark datasets, i.e., VQA1.0 and VQA2.0. Experimental results show that PIL can boost the accuracy of the existing VQA models (1.56%-2.93% gain) with a negligible increase in parameters (0.85%-5.4% parameters). Qualitative results also reveal the elimination of knowledge inertia in the existing VQA models after implementing our PIL.
New design of lightweight authentication protocol in wearable technology

Today, the use of wearable devices is becoming a thing inherent in the daily activities of urban communities. In practice, wearable communications may contain sensitive information regarding a user's health record, so authentication and confidentiality of data exchanged must be guaranteed. In addition, the success of authentication between users, wearable devices and smartphones is very important because there are various threats of attack on the authentication process. Based on previous studies, it was found that the security functionality of user impersonation attack is not owned by lightweight authentication protocols in the current wearable communication environment. So this research undertakes the design of a lightweight authentication protocol to be immune to user impersonation attacks to supplement the lack of security functionality in previous protocols with the support of performing a formal analysis using the Scyther Tool. The research method used is a Research Library supported by conducting protocol security test experiment. The developed protocol utilizes a modified and customized S-NCI key establishment protocol scheme to meet all targeted security functionality. The research resulted that the lightweight authentication protocol generated was immune to the impersonation attacks of users, then was able to add two new functionalities that added wearable devices and added smartphones.
Patch spaces and fusion strategies in patch-based label fusion

In the field of multi-atlas segmentation, patch-based approaches have shown promising results in the segmentation of biomedical images. In the most common approach, registration is used to warp the atlases to the target space and then the warped atlas labelmaps are fused into a consensus segmentation based on local appearance information encoded in form of patches. The registration step establishes spatial correspondence, which is important to obtain anatomical priors. Patch-based label fusion in the target space has shown to produce very accurate segmentations although at the expense of registering all atlases to each target image. Moreover, appearance (i.e., patches) and label information used by label fusion is extracted from the warped atlases, which are subject to interpolation errors. In this work, we revisit and extend the patch-based label fusion framework, exploring the role of extracting this information from the native space of both atlases and target images, thus avoiding interpolation artifacts, but at the same time, we do it in a way that it does not sacrifice the anatomical priors derived by registration. We further propose a common formulation for two widely-used label fusion strategies, i.e., similarity-based and a particular type of learning-based label fusion. The proposed framework is evaluated on subcortical structure segmentation in adult brains and tissue segmentation in fetal brain MRI. Our results indicate that using atlas patches in their native space yields superior performance than warping the atlases to the target image. The learning-based approach tends to outperform the similarity-based approach, with the particularity that using patches in native space lessens the computational requirements of learning. As conclusion, the combination of learning-based label fusion and native atlas patches yields the best performance with reduced test times than conventional similarity-based approaches.
Frequency Selection Approach for Energy Aware Cloud Database

A lot of cloud systems are adopted in industry and academia to face the explosion of the data volume and the arrival of the big data era. Meanwhile, energy efficiency and energy saving become major concerns for data centers where massive cloud systems are deployed. However, energy waste is quite common due to resource provisioning. In this paper, using dynamic voltage and frequency scaling (DVFS), a frequency selection approach is introduced to improve the energy efficiency of the cloud systems in terms of resource provisioning. In the approach, two algorithms, genetic algorithm (GA) and Monte Carlo tree search algorithm (MCTS), are proposed. A cloud database system is taken as an example to evaluate the approach. The results of the experiments show that both algorithms have its advantages. The algorithms have great scalability, in which the GA can be applied to thousands of nodes and the MCTS can be applied to hundreds of nodes. Both algorithms have high accuracy compared with optimal solutions (up to 99.9% and 99.6% for GA and MCTS, respectively). According to an optimality bound analysis, 26% of energy can be saved at most using our frequency selection approach.
Belief Change and Non-Monotonic Reasoning Sans Compactness

Belief change and non-monotonic reasoning are arguably different perspectives on the same phenomenon, namely, jettisoning of currently held beliefs in response to some incompatible evidence. Investigations in this area typically assume, among other things, that the underlying (background) logic is compact, that is, whatever can be inferred from a set of sentences X can be inferred from a finite subset of X. Recent research in the field shows that this compactness assumption can be dispensed without inflicting much damage on the AGM paradigm of belief change. In this paper we investigate the impact of such relaxation on non-monotonic logics instead. In particular, we show that, when compactness is not guaranteed, while the bridge from the AGM paradigm of belief change to expectation logics remains unaffected, the “return trip” from expectation logics to AGM paradigm is no longer guaranteed. We finally explore the conditions under which such guarantee can be given.
Temporal Planning with Temporal Metric Trajectory Constraints

In several industrial applications of planning, complex temporal metric trajectory constraints are needed to adequately model the problem at hand. For example, in production plants, items must be processed following a “recipe” of steps subject to precise timing constraints. Modeling such domains is very challenging in existing action-based languages due to the lack of sufficiently expressive trajectory constraints.We propose a novel temporal planning formalism allowing quantified temporal constraints over execution timing of action instances. We build on top of instantaneous actions borrowed from classical planning and add expressive temporal constructs. The paper details the semantics of our new formalism and presents a solving technique grounded in classical, heuristic forward search planning. Our experiments prove the proposed framework superior to alternative state-of-theart planning approaches on industrial benchmarks, and competitive with similar solving methods on well known benchmarks took from the planning competition.
Automated medical surgical trolley

Operating theatre is a place in a hospital where surgical operations are conducted on patients by surgeons. In the operating theatre, the surgical equipment is placed on stainless steel table or on surgical instrument tray. However, during the operation accidents can occur where the surgical tools placed near to the surgeon could be accidentally be hit by them during the surgical operation. This may cause the surgical tools to fall on the floor which may lead to injuries. Hence, this paper presents an automatic medical surgical trolley for surgeons to grab operating tools easily. The proposed system is implemented for automaticmedical surgical trolley movement using Arduino Uno R3. The invention provides an automatic medical surgical trolley which comprises automatic guidance, a wireless controller, an obstacle avoiding detection device, a touch screen controller via smart phone, an IP camera, a trolley, an integrated power supply and a processor. The trolley with stainless steel shelves is ideal for use in clinical environments and operation theatres. Medical equipment is loaded in the trolley, the wireless remote drives the trolley to move forwards and backwards. Automatic visual guidance is achieved via an IP camera attached to the trolley and a touch screen controller via a smart phone. A large amount of space and a large number of materials are saved, the workload of medical workers will be greatly relieved, and the working efficiency will be improved.
Learning Direct Optimization for scene understanding

We develop a Learning Direct Optimization (LiDO) method for the refinement of a latent variable model that describes input image x. Our goal is to explain a single image x with an interpretable 3D computer graphics model having scene graph latent variables z (such as object appearance, camera position). Given a current estimate of z we can render a prediction of the image g(z), which can be compared to the image x. The standard way to proceed is then to measure the error E(x, g(z)) between the two, and use an optimizer to minimize the error. However, it is unknown which error measure E would be most effective for simultaneously addressing issues such as misaligned objects, occlusions, textures, etc. In contrast, the LiDO approach trains a Prediction Network to predict an update directly to correct z, rather than minimizing the error with respect to z. Experiments show that LiDO converges rapidly as it does not need to perform a search on the error landscape, produces better solutions than error-based competitors, and is able to handle the mismatch between the data and the fitted scene model. We apply LiDO to a realistic synthetic dataset, and show that the method also transfers to work well with real images.
Corrections to “Fractal Intelligent Privacy Protection in Online Social Network Using Attribute-Based Encryption Schemes”

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Coreset Stochastic Variance-Reduced Gradient with Application to Optimal Margin Distribution Machine

A major problem for kernel-based predictors is the prohibitive computational complexity, which limits their application in large-scale datasets. Coreset, an approximation method which tries to cover the given examples with a small set of points, can be used to remain the prominent information and accelerate the kernel method. In this paper, we provide perhaps the first coreset-based kernel-accelerating optimization method that has a linear convergence rate, which is much faster than existing approaches. Our method can be used to train kernel SVM-style problems and obtain sparse solutions efficiently. Specifically, the method uses SVRG as the framework, and utilizes the core points to approximate the gradients, so it can significantly reduce the complexity of the kernel method. Furthermore, we apply the method to train ODM, a kernel machine enjoying better statistical property than SVM, so that we can reduce the risk of compromising the performance while encouraging the sparsity. We conduct extensive experiments on several large-scale datasets and the results verify that our method outperforms the state-of-the-art coreset approximation method in both efficiency and generalization, while simultaneously achieving significant speed-up compared to non-approximation baselines.
Learning Neural Bag-of-Matrix-Summarization with Riemannian Network

Symmetric positive defined (SPD) matrix has attracted increasing research focus in image/video analysis, which merits in capturing the Riemannian geometry in its structured 2D feature representation. However, computation in the vector space on SPD matrices cannot capture the geometric properties, which corrupts the classification performance. To this end, Riemannian based deep network has become a promising solution for SPD matrix classification, because of its excellence in performing non-linear learning over SPD matrix. Besides, Riemannian metric learning typically adopts a kNN classifier that cannot be extended to large-scale datasets, which limits its application in many time-efficient scenarios. In this paper, we propose a Bag-of-Matrix-Summarization (BoMS) method to be combined with Riemannian network, which handles the above issues towards highly efficient and scalable SPD feature representation. Our key innovation lies in the idea of summarizing data in a Riemannian geometric space instead of the vector space. First, the whole training set is compressed with a small number of matrix features to ensure high scalability. Second, given such a compressed set, a constant-length vector representation is extracted by efficiently measuring the distribution variations between the summarized data and the latent feature of the Riemannian network. Finally, the proposed BoMS descriptor is integrated into the Riemannian network, upon which the whole framework is end-to-end trained via matrix back-propagation. Experiments on four different classification tasks demonstrate the superior performance of the proposed method over the state-of-the-art methods.
A transfer learning algorithm for automatic requirement model generation

In this paper, we present a novel method for data-mining large informal product descriptions rather than extracting requirement features from proprietary project repositories. Our algorithm hybridizes deep-learning algorithms such as word2vec and recurrent neural network (RNN) with classical techni ques to improve the performance of text analysis. Given the inaccuracy and incompleteness of the software requirement descriptions on websites, the instance-transfer learning method is utilized to construct a robust classifier and predict domain feature knowledge based on domain knowledge similar to the target domain. The bagging clustering algorithm is utilized with multiple clustering algorithms to help select transfer instances. [Author to confirm changes.]The RNN-based algorithm is utilized as a useful alternative to predict missing features by studying the requirement descriptions of a related software system, while word2vec is utilized to extract sensible feature keywords for the specific software domain. [Author to confirm changes.]Our RNN model for every subclass is based on the clustering result, and we construct subclass classifiers to recommend requirement keywords. Requirement features recommended by our algorithm potentially increase opportunities for requirement classification, promote software requirement quality, and deliver more reliable software products. We explain the details of implementation and perform experimental work on real requirement descriptions to establish its worth.
Comparing geometric discord and negativity for bipartite states

The geometric discord $\mathcal{D}$ of a state is a measure of the quantumness of the state and the negativity $\mathcal{N}$ is a measure of the entanglement of a state. It was proved by D. Girolami and G. Adesso that for states on $\mathbb{C}^2\otimes\mathbb{C}^2$, the geometric discord is always greater than or equal to the square of the negativity and conjectured that this holds in general. S. Rana and P. Parashar showed that this relation does not hold for all states on $\mathbb{C}^2\otimes\mathbb{C}^n$ for $n>2$. We provide several analytic families of states on $\mathbb{C}^2\otimes\mathbb{C}^3$ violating this relation. Certain upper and lower bounds for $\mathcal{N}^2-\mathcal{D}$ are obtained for states on $\mathbb{C}^m\otimes\mathbb{C}^n$ for any $m, n\in\mathbb{N}$.
Cultural literacy building of primary school students as basic concept

The low literacy ability of the society will have an impact on the quality of human resources. Nowadays, literacy (read-write) has not yet become a necessity of life and part of nation's culture. Schools as the primary means of shaping the character of students need to plan programs that lead to literacy culture as a solution to the problem. Currently, schools are busy implementing government programs through literacy programs with the aim of the growing awareness of students in familiarizing the culture. This program provides the significant influence for students with various forms of activities undertaken by school. On this occasion, the writer tries to present some explanations related to the basic concepts, urgency, stapes of cultural development of literacy and the form of programs that can be done in supporting of literacy culture in Elementary School.
Adversarial Knowledge Representation Learning Without External Model

Knowledge representation learning, which embeds entities and relations of knowledge graph into low-dimensional vectors, is efficient for predicting missing facts. Knowledge graph datasets only store positive triplets. Nevertheless, negative cases are similarly crucial in knowledge representation learning. Conventionally, corrupted triplets are uniformly generated as negative cases, but actually, these corrupted triplets are heterogeneous. The majority of corrupted triplets are trivial, and they have limited influence on learning. Regarding the large number of corrupted triplet candidates, it is not efficient to train the model by uniformly generated corrupted triplets. Generative adversarial network (GAN)-inspired approaches are proposed to remit easily discriminated negative training examples, enabling faster and better convergence of the embedding models. Pre-trained external sampling models are required in these approaches. In this paper, we introduce a simple but strong negative sampling approach for adversarial knowledge representation learning, named loss adaptive sampling mechanism, which is efficient without an external sampling model. Furthermore, false negative cases are always over-trained in the training stage with efficient negative sampling approaches. We propose a push-up mechanism and verify whether it is feasible to alleviate these over-trained false negative cases. The experimental results show that our adversarial knowledge representation learning approach outperforms the GAN-based sampling method—KBGAN.