Reviewerly/exHarmony huggingface dataset:
Interfaces for Modular Reasoning in Context-Oriented Programming

Different activation mechanisms for Context-Oriented Programming (COP) like implicit activations have been proposed, increasing COP opportunities to be applied in real scenarios. However, activation mechanisms and base code definitions are insufficiently decoupled, as conditionals to activate layers require base code variable references. This hinders reuse, evolution, and modular reasoning of COP and base code, and therefore, uses of COP in real scenarios. This paper proposes interfaces, which are shared abstractions to communicate activation mechanisms and base code in a decoupled manner. Using these interfaces, an object can exhibit its internal state and behaviors, and conditionals use them to (de)activate layers. As layers are planned to be (re)used in different applications, developers can use interfaces to overcome the incompatibility between values exposed by a particular base code and values required by a layer. In addition, as a layer is a plain object, it can use an interface to exhibit the conditional evaluation of its activation to other layers to resolve conflicts among activations of layers. We apply this proposal to implicit activations in which evaluations of conditionals implicitly (de)activate layers. Finally, we illustrate the benefits of this proposal through RI-JS, a practical JavaScript library that currently supports interfaces, reactive activations (implementation variant for implicit activations), global and dynamic deployment, enter and exit transition processes, and partial methods.
Time-Specific Signatures

In Time-Specific Signatures (TSS) parameterized by an integer $$T\in \mathbb {N}$$ , a signer with a secret-key associated with a numerical value $$t\in [0,T-1]$$ can anonymously, i.e., without revealing t, sign a message under a numerical range [L, R] such that $$0\le L \le t\le R\le T-1$$ . A direct application of TSS is anonymous questionnaire, where each user associated with a numerical value such as age, date, salary, geographical position (represented by longitude and latitude), etc., can anonymously fill in a questionnaire in an efficient manner. In this paper, we propose two polylogarithmically efficient TSS constructions based on an asymmetric pairing with groups of prime order, which achieve different characteristics in efficiency. In the first one based on a forward-secure signatures scheme concretely obtained from a hierarchical identity-based signatures scheme proposed by Chutterjee and Sarker (IJACT’13), size of the master public-key, size of a secret-key and size of a signature are asymptotically $$\mathcal {O}(\log T)$$ , and size of the master secret-key is $$\mathcal {O}(1)$$ . In the second one based on a wildcarded identity-based ring signatures scheme obtained as an instantiation of an attribute-based signatures scheme proposed by Sakai, Attrapadung and Hanaoka (PKC’16), the sizes are $$\mathcal {O}(\log T)$$ , $$\mathcal {O}(1)$$ , $$\mathcal {O}(\log ^2 T)$$ and $$\mathcal {O}(\log T)$$ , respectively.
Sentiment Analysis of Consumer-Generated Online Reviews of Physical Bookstores Using Hybrid LSTM-CNN and LDA Topic Model

Physical bookstore is the leader of cultural trend, the carrier of national reading and the provider of public cultural services, which embodies the cultural soft power of a city. The widely use of Internet e-commerce platform and the change of people's reading habits have brought great impact on physical bookstores, resulting in poor overall profitability of physical bookstores. In order to realize the sustainable development of physical bookstores, we mine and analyze consumer-generated online reviews. In this paper, a method of sentiment analysis based on Hybrid LSTM-CNN (Hybrid Long Short-Term Memory-Convolutional Neural Network) and LDA (Latent Dirichlet Allocation) topic model is proposed. Firstly, the Hybrid LSTM-CNN model is used to classify reviews, and then LDA topic model is used to extract features of positive and negative reviews. The results show that Hybrid LSTM-CNN model has better performance than the classic LSTM and CNN in sentiment classification. The LDA model mines that consumers have the positive attitude towards the products, context and ambiance of physical bookstores, and the negative attitude towards price and service. This method studies consumer-generated online reviews in physical bookstores from two aspects: sentiment classification and topic mining, which can help physical bookstore operators to know consumer feedback in time.
Privacy-Preserving GWAS Computation on Outsourced Data Encrypted under Multiple Keys Through Hybrid System

Genome-wide association studies (GWAS) necessitate genomic information of a large population of an individual to achieve reliable results. The human genome can expose sensitive information and is potentially re-identifiable, which raises privacy and security concerns, making individuals deter from contributing their genomic information for such studies. Therefore, there is a need for secure and efficient analysis in which the data owners can securely allocate both the computation and storage on the untrusted cloud. Previous solutions used either a single key setting or far from being practical. Also, efficiency is a big limitation for real life applications. In this paper, we propose a novel hybrid solution that uses the concept of multi-key homomorphic encryption to encrypt ciphertexts using different public keys. To decrypt all the ciphertexts by using a single secret key, we add the re-encryption property of proxy re-encryption to multi-key homomorphic encryption. Also, our framework uses the recently introduced hardware-based architecture (i.e software guard extensions) to securely perform GWAS on genomic data in a privacy-preserving manner and ensure high efficiency by speeding up the computation while preserving the privacy of the data owners. To the best of our knowledge, our scheme is the first multi-key homomorphism combined with intel SGX for analyzing genomic data.
Simulation Research on Pipeline Map System Based on Multi-robot Queue Cooperation

The research on the map construction of pipeline environment is of great significance for the location of pipeline network, pipeline network planning and pipeline maintenance. It is an important development direction of underground pipeline detection technology to use robot to carry detection equipment into the pipeline to complete fault detection. In this paper, we propose a method of multi-robot collaborative mapping based on decentralized exploration and centralized mapping. The robot describes the pipeline information in the manner designed in this paper. The upper master gets the pipeline map information through single robot and then describes it by using geometry topology method. The simulation results shows that the final pipeline map are consistent with the actual pipeline map.
Acmeological determinants of experts selections for “mega science” class projects

Abstract The article presented is the continuation of the development of psychological and acmeological features of training managers, experts, specialists for projects of the “mega science” class. The need to use the achievement of qualifier science in order to increase the reliability and accuracy of measurement of various parameters, which have a significant impact on the effectiveness of professional activities, is proved. On the basis of a competent approach, a model of the subject of personnel qualifiers - an expert has been developed. The system of competences that an expert should have for “mega science” class projects is described. It is substantiated that the model of the subject of personnel qualifiers should meet, in addition to traditional, a number of additional criteria reflecting the conditions and specifics of professional activities, which are the basis for the formation of key acmeological determinants of the selection of experts for “mega science” class projects. In addition, it is noted that the distinctive feature of the subject of personnel qualifiers is the obligatory achievement of the expert professional “akme” in his professional field, within the framework of “mega science” class projects.
Evaluation of attack capability of UAV intelligent swarm based on AHP Fuzzy Evaluation

Abstract In order to evaluate the attack ability of UAV intelligent swarm, the evaluation index system of UAV intelligent swarm attack capability is established, the factors influencing the attack capability of UAV intelligent swarm are analyzed, the capability evaluation model is established, and the weight of each factor is determined by AHP, and finally the effectiveness index is evaluated by fuzzy evaluation matrix [1]. The results show that the evaluation results are objective and reasonable, which can provide theoretical reference for the optimal design and use of UAV intelligent swarm system.
Key Nodes Cluster Augmented Embedding for Heterogeneous Information Networks

Heterogeneous Information Networks (HINs), composed of multiple types of node and relation, usually have more expressive ability for complex relational data. Recently, network embedding aiming to project the network into a low-dimensional vector space has received much attention. Most existing embedding methods for HINs utilize meta-path to capture the proximity of node. However, these methods usually ignore the inequivalence of different types of node and clustering structure of network, which are important characteristics of HINs. Hence, we propose a key node based heterogeneous network embedding method enhanced by the clustering information. In our method, we first utilize a meta-path guided random walk to obtain general node representations in terms of rich heterogeneous semantic features in HINs. To indicate different equivalence of nodes, we define key nodes which are usually in the essential location in HINs, such as paper in the bibliographic HINs. Afterwards, we incorporate the clustering structure of the key nodes into network embedding learning via Gaussian Mixture Model to further enhance the representations of nodes. Lastly, we design a unified objective function to mutually learn the two parts effectively. Extensive experiments are conducted and the results validate the effectiveness of our model.
Design of culinary information system based on android using multimedia development life cycle

Abstract The development of smartphone application technology makes it easier for people to obtain information without being limited by space and time. One of them is the development of an Android smartphone-based culinary information system application that allows culinary information to be accessed anywhere and anytime online with the hope that it can be utilized in the field of marketing and promotion to increase sales and revenue. This article aims to design an Android-based culinary information system application to make it easier to provide culinary information to the public. The methodology used is a multimedia development life cycle with stages covering, concept, design, collecting, assembly, testing, and distribution materials. The results of this study are in the form of android-based culinary information applications. Contributions given by this application can provide typical culinary information from various regions, especially in Indonesia.
Comparison of Classification C4.5 Algorithms and Naïve Bayes Classifier in Determining Merchant Acceptance on Sponsorship Program

Abstract The large number of merchants that make sponsorship held by the Bank reaches thousands, data mining is used to classifying thousands of data. Naïve Bayes algorithm and C 4.5 are classification algorithms in data mining. The classification results are used as determinant where the merchant deserves to receive the sponsorship program, which potentially provides the source of funds and increase the brand awareness of the company by looking at the performance, transaction amount, total nominal, average daily transaction, average transaction nominal. Comparison results show that The C 4.5 algorithm is the best model for handling case of Merchant eligibility in the Sponsorship Program. This can be proved by looking at the level of accuracy generated on the testing and validation process of the model. Both models have the same AUC value but the C 4.5 algorithm produces a superior accuracy value with a difference of 0.45% compared to Naïve Bayes.
Adaptive Enterprise Architecture: Initiatives and Criteria

Due mainly to digital transformation, enterprises are immersed in dynamic environments. They are dealing with internal and external changes (gradual or abrupt, frequent or occasional, in the short or long term) that impact various parts of their Enterprise Architecture. To cope with the requirements of this new digital era, agility and adaptation are compulsory. Though Enterprise Architecture (EA) was designed as a catalyst for change management, traditional Enterprise Architecture approaches do not rely on well-defined concepts, lack agility and are constrained by layered architecture (business layer, functional layer, deployment layer, etc.). Thus, a new generation of EA, allowing continuous self-improvement and proactive adaptation to better match the new conditions, is required. In this paper, we explore and investigate some approaches of EA that deal with change management. Then, we, particularly, note the various definitions of the notion of adaptive EA, according to literature. Finally, we suggest some criteria, regarding the adaptation of EA, to highlight the limitations of the approaches analyzed.
A new 3D Viewer system based on hand gesture recognition for smart interaction

The visualization of the 3D models is a scorching topic in computer vision and human-computer interaction. The demands for 3D models have been increased due to high involvement in animated characters, virtual reality and augmented reality. To interact with 3D models with the help of mouse and keyboard is a very hectic, less efficient and complex process because of multiple types of operations required by the models to view properly in all sides. So it is essential to improve the user interaction with the 3D system. In this paper, a new method is introduced by using the Microsoft Kinect v2 to detect the human body and joints. First, we trained the Kinect to understand the specific gestures, and then recognize to perform the specific task on an object in the proposed environment.
Interpretability and Refinement of Clustering

The difficulty to validate clustering reliability hinders the adoption of clustering in real-life applications. We propose: (a) a set of symbolic representations to interpret problem spaces and (b) the CluReAL algorithm to refine any clustering result regardless of the used technique. Both approaches are grounded by recently published absolute cluster validity indices. Conducted experiments show how the refinement algorithm improves performances in a wide variety of scenarios and builds more interpretable solutions, whereas symbolic representations are shown to offer explainable summaries of problem contexts. Refinement and interpretability are both crucial to reduce failure and increase performance control and operational awareness in processes that depend on clustering.
Action Recognition in Videos Using Global Descriptors and Pre-trained Deep Learning Architecture

A motion energy image (MEI) is a spatial template that collapses regions of motion into a single image in which more moving pixels are brighter than others. The forward single-step history image (fSHI) is a spatiotemporal template that shows the presence and direction of motion. Each video can be described using these templates. Recently, the advantage of deep learning architectures for human activity recognition encourages us to explore the effectiveness of combining them with these templates. Based on this opinion, a new method is proposed in this paper. Each video is split into N groups of consecutive frames, and the MEI and fSHIs are computed for each group. Transfer learning with the fine-tuning technique has been used for classifying these templates separately. By fusing the two streams of softmax scores, the final decision is acquired. This proposed method achieves the recognition accuracies of 92.60% and 93.40% for UCF Sport and UCF-11 action datasets, respectively. Also it is compared with state-of-the-art approaches and the results show that the proposed method has the best efficiency.
Handwriting Prediction Considering Inter-Class Bifurcation Structures

Temporal prediction is a still difficult task due to the chaotic behavior, non-Markovian characteristics, and nonstationary noise of temporal signals. Handwriting prediction is also challenging because of uncertainty arising from inter-class bifurcation structures, in addition to the above problems. For example, the classes `0' and `6' are very similar in terms of their beginning parts; therefore it is nearly impossible to predict their subsequent parts from the beginning part. In other words, `0' and `6' have a bifurcation structure due to ambiguity between classes, and we cannot make a long-term prediction in this context. In this paper, we propose a temporal prediction model that can deal with this bifurcation structure. Specifically, the proposed model learns the bifurcation structure explicitly as a Gaussian mixture model (GMM) for each class as well as the posterior probability of the classes. The final result of prediction is represented as the weighted sum of GMMs using the class probabilities as weights. When multiple classes have large weights, the model can handle a bifurcation and thus avoid an inaccurate prediction. The proposed model is formulated as a neural network including long short-term memories and is thus trained in an end-to-end manner. The proposed model was evaluated on the UNIPEN online handwritten character dataset, and the results show that the model can catch and deal with the bifurcation structures.
A Multimodal Clustering Framework With Cross Reconstruction Autoencoders

Multimodal clustering algorithms partitions a multimodal dataset into disjoint clusters. Common feature extraction is a key part in multimodal clustering algorithms. Recently, deep neural networks shows high performance on latent feature extraction. However, existing works did not completely explore the cross-model distribution similarity utilizing deep neural networks. We present a deep multimodal clustering framework with cross reconstruction. Feature extraction apply global cross reconstruction and local cross reconstruction respectively to enforce early fusion among different modalities. Analysis shows that the both cross reconstruction networks reduces the Wasserstein distance of latent feature distributions, which indicates that the proposed framework ensures the distribution similarity of common latent features. Experimental results on benchmark datasets demonstrate superiority beyond existing works.
Adversarial Training with Bi-directional Likelihood Regularization for Visual Classification

Neural networks are vulnerable to adversarial attacks. Practically, adversarial training is by far the most effective approach for enhancing the robustness of neural networks against adversarial examples. The current adversarial training approach aims to maximize the posterior probability for adversarially perturbed training data. However, such a training strategy ignores the fact that the clean data and adversarial examples should have intrinsically different feature distributions despite that they are assigned with the same class label under adversarial training. We propose that this problem can be solved by explicitly modeling the deep feature distribution, for example as a Gaussian Mixture, and then properly introducing the likelihood regularization into the loss function. Specifically, by maximizing the likelihood of features of clean data and minimizing that of adversarial examples simultaneously, the neural network learns a more reasonable feature distribution in which the intrinsic difference between clean data and adversarial examples can be explicitly preserved. We call such a new robust training strategy the adversarial training with bi-directional likelihood regularization (ATBLR) method. Extensive experiments on various datasets demonstrate that the ATBLR method facilitates robust classification of both clean data and adversarial examples, and performs favorably against previous state-of-the-art methods for robust visual classification.
A Feature Selection Approach to Visual Domain Adaptation in Classification

In machine learning, we presume datasets to be labeled while performing any operation. But, is it true in real-life scenarios? To its contrary, we have an enormous amount of unlabeled datasets available in the form of images, videos, audios, articles, and many more. The major challenge we face is to train our classification model with primitive machine learning algorithms because these algorithms only expect labeled data. To overcome these limitations visual domain adaptation algorithms such as MEDA (Manifold Embedded Distribution Alignment) have been introduced. The main motto of MEDA is to minimize the distribution difference between the source domain (an application that contains enough labeled data) and the target domain (an application that contains only unlabeled data). In this way, the source domain labeled data can be utilized to enhance the performance of the target domain classifier. Though MEDA (Manifold Embedded Distribution Alignment) approach shows remarkable improvement in classification accuracy, but still there is considerable scope of improvement. There are plenty of irrelevant features in both domains. These irrelevant features create a hole for this algorithm and prevent the target domain classifier from becoming more robust. Therefore, for the purpose of filling this hole, we propose a new feature selection based visual domain adaptation (FSVDA) method which uses particle swarm optimization (PSO), where the MEDA method is considered as a fitness function that leads to automatically select a good subset of features over both the domains. Extensive experimental results on two real-world domain adaptation (DA) data sets such as object recognition and digit recognition demonstrate that our proposed method outperforms state-of-the-art primitive and domain DA algorithms. It is a big challenge to train the classifier for a new unlabeled image dataset in image classification and computer vision. The two magnificent solutions to this challenge are transfer learning and domain adaptation. By transfer learning, we can use our knowledge from previously trained models for training newer models.
What is the Reward for Handwriting? — A Handwriting Generation Model Based on Imitation Learning

Analyzing the handwriting generation process is an important issue and has been tackled by various generation models, such as kinematics based models and stochastic models. In this study, we use a reinforcement learning (RL) framework to realize handwriting generation with the careful future planning ability. In fact, the handwriting process of human beings is also supported by their future planning ability; for example, the ability is necessary to generate a closed trajectory like `0' because any shortsighted model, such as a Markovian model, cannot generate it. For the algorithm, we employ generative adversarial imitation learning (GAIL). Typical RL algorithms require the manual definition of the reward function, which is very crucial to control the generation process. In contrast, GAIL trains the reward function along with the other modules of the framework. In other words, through GAIL, we can understand the reward of the handwriting generation process from handwriting examples. Our experimental results qualitatively and quantitatively show that the learned reward catches the trends in handwriting generation and thus GAIL is well suited for the acquisition of handwriting behavior.