Reviewerly/exHarmony huggingface dataset:
Cross-Domain Sentiment Analysis of the Natural Romanian Language

Sentiment analysis is one of the hot topics nowadays in business and economics. It opens the door for economists to powerful unobserved information hidden in vast amounts of unstructured text which sometimes is vital in explaining or even predicting events. Understanding sentiments expressed freely in a speech or piece of text is, however, not an easy task, especially when it comes to crossing the barrier of a domain of major interest or of a wide-spread language, such as English. Finding the appropriate means to compute a sentiment score for languages with complex morphology, such as Romanian, has become a necessity for accurate and efficient sentiment analysis at a national level. This study aims to fill this gap by compiling a cross-domain dataset and comparing the most promising machine-learning classification techniques on top of it: Logistic Regression, Naïve Bayes, Support Vector Machine, Decision Trees, Recurrent Neural Network, and Transformers. The 98% training and 93% validation accuracies reveal the fitness of BERT (Bidirectional Encoder Representations from Transformers) for Romanian colloquial text and are synthesized in a model that could be further used for sentiment polarity predictions.
Random Sharing Parameters in the Global Region of Convolutional Neural Network

Building an efficient model with a compact structure and less parameters while preserving its competitive performance is meaningful in the field of neural networks. Traditionally, a unique group of parameters is identified for each convolution layer. Inspired by the universal approximation theorem, in this study, we explore a flexible way to configure the parameters of a convolutional neural network model. First, we set a parameter pool that stores a certain number of parameters, through which we can also control the number of parameters of a neural model. Second, we randomly select a group of continuous position parameters from the pool for each convolution layer. Finally, we perform extensive experiments for the standard architectures of the ResNet and DenseNet on several benchmark datasets. In the experiments, on CIFAR-10, most of the models could perform almost as well as the original ones within a 0.7% decline. On the difficult tasks CIFAR-100 and ImageNet, most of the models perform a little less within an approximately 1.5% decline. In this study, we extend weight sharing from the interior of one feature map to any layers, through which we can control the number of parameters of a neural model.
Fast Object Tracking Using Micro Aerial Vehicles

The development of robust and applicable visual object tracking algorithms for surveillance purposes is among the most studied applications of aerial robots. The tracking problem is mostly considered as an open loop system, with no control on the tracker camera motion. In the few exiting closed-loop approaches, the visual feedback is typically expressed in pixel coordinates of image, leading to poor performance in fast motions due to the coupling problems of flying robot’s states. In this paper, we introduce a fast motion planning for the flying camera that is based on visual tracking feedback in the inertial coordinates by incorporating kinematics model of the robot in motion planning. The proposed strategy is shown to resolve visual object tracking challenges by maintaining object scale and perspective, and reducing the chance of occlusion and illumination change, thus increasing the tracking durability. As a result, the flying robot is capable of tracking a person running at speeds up to 5 meters per second, with no predetermined paths, a task which requires rather visually challenging maneuvers for the flying robot.
Design of a Novel Neural Network Compression Method for Tiny Machine Learning

Traditional IoT processing data is sent from local devices to the cloud for processing, which has disadvantages such as low privacy, high latency, and low energy efficiency. These drawbacks can be effectively remedied by deploying the model for processing on devices at the "edge" of the cloud. In order to realize the data being processed directly on the cloud "edge" devices, the original machine learning algorithms need to be improved. One of the important steps is neural network compression.
Learned Multi-Field De-Interlacing with Feature Alignment via Deformable Residual Convolution Blocks

Deinterlacing continues to be an important problem of interest since many digital TV broadcasts and catalog content are still in interlaced format. Although deep learning has had huge impact in all forms of image/video processing, learned deinterlacing has not received much attention in the industry or academia. In this paper, we propose a novel multi-field deinterlacing network that aligns features from adjacent fields to a reference field (to be deinterlaced) using deformable residual convolution blocks. To the best of our knowledge, this paper is the first to propose fusion of multi-field features that are aligned via deformable convolutions for deinterlacing. We demonstrate through extensive experimental results that the proposed method provides state-of-the-art deinterlacing results in terms of both PSNR and perceptual quality.
A novel Bayesian inference based training method for time series forecasting

Bayesian inference shows that future event distribution not only depends on the past events (prior), but also depends on the relation between the past and the future events (likelihood). However, the classical Bayesian methods do not consider the important contributions of recent data.In this paper, we propose a new Bayesian inference based training method, which can be used as on-line training for Bayesian methods. We give the training methods for the exponential and the normal models. We successfully apply this method for the seismic parameter prediction using the data of central Italy from years 2014 to 2017. Comparisons on disscuss section in this work show our method is more effective than the other Bayesian methods.
Non-strict Attentional Region Annotation to Improve Image Classification Accuracy

Although convolutional neural networks (CNNs) have been widely used in image classification, a large amount of training data is required for training models. Especially in industrial applications, not only is there a demand for reducing the cost of labeling images, but also, it is difficult to obtain a large amount of training images. Several approaches have been proposed to improve classification accuracy by embedding human knowledge other than labels into the models. However, in these approaches, the annotation methods tend to become complex and applicable only to specific model architectures corresponding to those methods.In this study, we propose a new approach to improve the image classification accuracy of CNNs by adding human knowledge as simple region annotations. In our method, annotators draw the regions that attract the most attention as a single ellipse per image for classifying image categories. This annotation is called non-strict attentional region annotation (NARA) and is used for training CNNs together with the labels. We also propose a training method using NARA, which is applicable to common CNNs. For the proof of concept, we have added region annotations to all 5,000 images of the STL-10 training dataset. Through experiments, we demonstrate that our approach is effective to improve image classification accuracy with relatively inexpensive additional cost. Our annotation dataset is available at: https://git.io/stl10nara.
Modulation Recognition Based on Lightweight Residual Network via Binary Quantization

Signal modulation recognition is an important and complicated problem in spectrum monitoring. In recent years, many advanced deep learning methods have been proposed for this problem. However, the high computational cost of deep neural networks largely hinders practical deployment. In this paper, we consider the signal modulation recognition problem via the deep residual network and propose a binary quantization scheme to reduce the model size and memory cost. This is achieved by simultaneously increasing shortcuts and maintaining full-precision weights and outputs of key layers in binary residual network in a simple way. The results show that with competitive accuracy, 95% of multiply–accumulate operations of model are converted into binary operations under 0 dB snr. In summary, the proposed method can effectively reduce the parameters and complexity of recognition model.
A Synset-Based Recommender Method for Mixed-Initiative Narrative World Creation

A narrative world (NW) is an environment which supports enacting a given story. Manually creating virtual NWs (e.g. for games and films) requires considerable creative and technical skills, in addition to a deep understanding of the story in question. Procedural generation methods, in turn, generally lack in creativity and have a hard time coping with the numerous degrees of freedom left open by a story. In contrast, mixed-initiative approaches offer a promising path to solve this tension. We propose a mixed-initiative approach assisting an NW designer in choosing plausible entities for the locations, where the story takes place. Our approach is based on a recommender method that uses common and novel associations to narrative locations, actions and entities. Our method builds upon a large dataset of co-occurrences of disambiguated terms that we retrieved from photo captions. Building on this knowledge, our solution deploys entity (un)relatedness, offers clusters of semantically and contextually related entities, and highlights novelty of recommended content, thus effectively supporting the designer’s creative task, while helping to stay consistent with the story. We demonstrate our method via an interactive prototype called roleTaleForge. Designers can obtain meaningful entity suggestions for their NWs, which enables guided exploration, while preserving creative freedom. We present an example of the interactive workflow of our method, and illustrate its usefulness.
Weighted matrix-object data clustering guided by matrix-object distributions

In data mining, the input of most algorithms is a data set in which each example is a feature vector. However, in many real applications an example usually contains multiple feature vectors and its observed classification is the responsibility of all feature vectors. We call this example kind matrix-object. Some existing clustering algorithms for matrix-object data fail to consider contributions of attributes to clusters, which may degrade clustering solutions due to less discriminative attributes. Some existing clustering algorithms for the data in which each example is a vector consider the contributions but encounter difficulties in handling matrix-object data. For matrix-object data, ordered and cross matrix-object distributions may exist in a cluster and cause different ways of measuring qualities of clusters. In this paper, we propose a weighted matrix-object data clustering algorithm guided by matrix-object distributions. We define cluster and matrix-object compactness respectively for the two distributions to measure qualities of clusters. The bigger the compactness is, the higher the quality is. So the proposed algorithm utilizes the compactness to assign a weight to each attribute for each cluster and maximizes weighted cluster and matrix-object compactness to find the optimal weight and the final clustering partition. Furthermore, a regular term about weight is added to the objective function to make more higher discriminative attributes participate in the optimization. Experimental results on real data have shown the effectiveness of the proposed algorithm. Compared with previous clustering algorithms, the proposed algorithm improves the clustering performance and enhances the interpretability of clustering results.
User Selection Strategies of Interactive Data Visualization Format

A key feature of interactive data visualization is the ability for users to choose the visualization formats and chart types. Some users may rely on their experience and gut feelings in selecting a visualization format while other users may evaluate the task characteristics and select a visualization format that fits better with task requirements. In this study, we examine the conditions under which users employ the experience or analytical strategies in selecting visualization format and the effects of the two strategies on decision accuracy and confidence. A laboratory study has been conducted to test the hypotheses. Our results show that more users employ the analytical strategy when confronted with incongruent cognitive style and task difficulty conditions and they were more confident about their decisions following the analytical strategy. The study helps us understand how interactive data visualizations can be utilized and identify areas for data visualization education and training.
Leveraging on Cross Linguistic Similarities to Reduce Grammar Development Effort for the Under-Resourced Languages: a Case of Kenyan Bantu Languages

Rule-based grammar development is labor-intensive in terms of time and knowledge requirements, especially for complex morphology and under-resourced languages. Notwithstanding, these grammars are needed for deep natural language processing, generation of well-formed output, or both. To address the challenge, this paper seeks to develop shared multilingual wide-coverage grammar for a subset of Kenyan Bantu languages in Grammatical Framework (GF) by leveraging on cross linguistic similarities using the grammar engineering strategies: grammar porting and grammar sharing. The shared grammar was developed using the morphology-driven approach, where the lexicons are defined first, followed by inflection regular expression and finally the syntax production rules. The resulting congruent Bantu parameterized grammar had shareability for category linearizations, parameters, paradigms, and syntax rules of 100%, 68.75%, 65.3% and 89.57%, respectively, while portability (modification) was exhibited in paradigms, parameter plus syntax rules at 14.29%, 18.75% and 10.43% respectively. The research concludes leveraging on the cross-linguistic similarities of principles and parameters significantly reduces multilingual grammar's development effort and contributes by developing the Bantu parametrized grammar which demonstrates how the effort of developing the rule base has been significantly reduced in languages where data is a scarce commodity.
Constraint-embedded paraphrase generation for commercial tweets

Automated generation of commercial tweets has become a useful and important tool in the use of social media for marketing and advertising. In this context, paraphrase generation has emerged as an important problem. This type of paraphrase generation has the unique requirement of requiring certain elements to be kept in the result, such as the product name or the promotion details. To address this need, we propose a Constraint-Embedded Language Modeling (CELM) framework, in which hard constraints are embedded in the text content and learned through a language model. This embedding helps the model learn not only paraphrase generation but also constraints in the content of the paraphrase specific to commercial tweets. In addition, we apply knowledge learned from a general domain to the generation task of commercial tweets. Our model is shown to outperform general paraphrase generation models as well as the state-of-the-art CopyNet model, in terms of paraphrase similarity, diversity, and the ability to conform to hard constraints.
Conducting a ‘manual examination’ of a device as part of a digital investigation

As those tasked with investigating incidents regularly encounter the need to examine content on associated digital devices, there is a need to ensure this task is carried out both effectively and efficiently. In addition to the traditional digital forensic examination workflow, in some cases first responders/investigators have the option to ‘manually examine’ a device live, in order to quickly identify and evaluate any valuable content it may contain. This process may have particular benefits when dealing with inquiries at scene. In this paper, the concept of a device ‘manual examination’ is discussed, including what it is and how it can be applied. A ‘device manual examination procedure’ is proposed - a framework for supporting the first responder/investigator in determining when a device manual examination is a viable device interrogation option as part of their inquiries, and to ensure they are effectively prepared to conduct it. This work is intended to support operational first responders/investigators to conduct device manual examinations appropriately.
The Contribution of the Neural Network to the Improvement of Speech Recognition

Neural Network are one of the artificial intelligence techniques. Work of the neural network inspired by biological neural networks and have applied in many fields, the most important of which is the communication between the computers and humans (Human-Computer Interaction) naturally and simple such as speech or gesture communication etc. In this article, we present an overview of automatic speech recognition by neural networks. We start with the history of neural networks. Then will present some recent research, their results, and the techniques used and conclude with a discussion of this research with the illustration of the intelligent interface model that depends on neural networks.
Anchors-Based Incremental Embedding for Growing Knowledge Graphs

Knowledge graph embedding aims to transform the entities and relations of triplets into the low-dimensional vectors. Previous methods are oriented towards the static knowledge graphs, in which all entities and relations are assumed to be known and only some unknown triplets need to be predicted. However, the real-world knowledge graphs can grow dynamically, and some new knowledge are often added. To embed the new knowledge into the space of original knowledge graph, the classic models have to perform the entire re-embedding with including the new and original knowledge. This causes heavy computational burden for embedding. To address this problem, this study proposes a new model of anchors-based incremental embedding (ABIE) to implement the dynamical embedding for the growing knowledge graph. According to ABIE, every knowledge graph has some key entities, called anchors, which can fix the embedding space of knowledge graph. When some new knowledge is added into the graph, only a few updated entities and relations are embedded into the embedding space with the help of anchors, and the entire re-embedding on the whole graph is not necessary. By this way, the computational burden of embedding caused by the growth of knowledge graph is reduced significantly.
Artificial Neural Networks in Export and Import Forecasting: An Analysis of Opportunities

The paper concerns the issue of forecasting trends in trade relations between Malaysia and Ukraine using artificial neural networks. The current state of trade relations between the countries in the context of the COVID-19 pandemic has been analyzed. Considering the advantages and disadvantages of the types of neural networks built into the software product STATISTICA 10, MLP network has been chosen to build a predictive model of imports and exports of goods. The forecast values for the volumes of exports and imports of goods for the period from January 2021 to December 2022 have been calculated. Comparing the results, the researchers concluded that the artificial neural network is the most successful model for forecasting imports and exports. Suggestions for effective evaluation and forecasting of international trade indicators using the theory of time series and neural network technologies are given and the directions of further scientific research arising from this paper are formed.
Transformation Architecture for Multi-Layered WebApp Source Code Generation

The evolution of Web technologies leads to software premature obsolescence requiring technology-independent representations to increase the reuse rates during the development process. They also require integration into service-oriented architectures to exchange information with different Web systems supporting runtime interoperability. Web Applications (WebApps) run on devices with different capabilities and limitations increasing the complexity of the development process. To address these challenges, different proposals have emerged to facilitate the development of WebApps, which is still an open research field with many challenges to address. This paper presents a model transformation architecture based on software standards to automatically generate full stack multi-layered WebApps covering Persistence, Service, and Presentation layers. This transformation architecture also generates the set of test cases to test WebApp business logic. The proposed transformation architecture only requires a UML platform-independent class model as an input to generate fully functional Web applications in a three-tier architecture including the three layers, while most proposals focus on the generation of the Presentation layer. In addition, this architecture employs software industry standards to enable an easy integration into third-party tools and development environments. The transformation Architecture proposed has been empirically validated on the case study of a fully functional travel management WebApp that is generated using a UML class diagram employing a third-party tool integrated into the same integrated development environment.
Group Key Exchange Compilers from Generic Key Exchanges

We propose a group key exchange compiler using any two-party key exchange for which the shared key space is the subset of a group and whose security reduces to a decisional hard problem, such that the security of the group key exchange relies on the security of the two-party key exchange and, in turn, the hardness of the underlying decisional problem.This work is a generalization of the multicast Burmester-Desmedt group key exchange in a modified G-CK\(^+\) security model.For n parties, the group key exchange protocol has constant round complexity and communicational complexity \(O(\log _2 n)\). We also present a peer-to-peer version with round complexity \(O(\log _2 n)\) and constant communicational complexity.
Effectively and Efficiently Defending Shadowsocks against Website Fingerprinting Attacks

Shadowsocks is one of the most popular encryption proxies. It is designed with few defense methods against website fingerprinting attack (WFP). WFP can identify user accessed websites by analyzing Shadowsocks traffic. Existing studies defending Shadowsocks against WFP generally pad or insert packets to minimize statistical differences between accesses to different websites. They can achieve promising denfense effects, yet at the cost of large time delay and bandwidth overhead. Therefore, it is necessary to design an effective and efficient algorithm to defend Shadowsocks against WFP. In this paper, we propose two algorithms, namely, PadS and PadI, which are based on the size and the true distribution of time series of traffic data packets, respectively. The results show that PadI and PadS can substantially degrade the attack performance while reducing bandwidth consumption to 34% and 32%, respectively, and the combination of the two algorithms outperforms existing defense algorithms.