Reviewerly/exHarmony huggingface dataset:
Interpretable Neural Computation for Real-World Compositional Visual Question Answering

There are two main lines of research on visual question answering (VQA): compositional model with explicit multi-hop reasoning, and monolithic network with implicit reasoning in the latent feature space. The former excels in interpretability and compositionality but fails on real-world images, while the latter usually achieves better performance due to model flexibility and parameter efficiency. We aim to combine the two to build an interpretable framework for real-world compositional VQA. In our framework, images and questions are disentangled into scene graphs and programs, and a symbolic program executor runs on them with full transparency to select the attention regions, which are then iteratively passed to a visual-linguistic pre-trained encoder to predict answers. Experiments conducted on the GQA benchmark demonstrate that our framework outperforms the compositional prior arts and achieves competitive accuracy among monolithic ones. With respect to the validity, plausibility and distribution metrics, our framework surpasses others by a considerable margin.
Extremal states of qubit-qutrit system with maximally mixed marginals

We study extremal elements of the convex set of qubit-qutrit states whose marginals are maximally mixed. In the two qubit case, it is known that every extreme state of such a convex set is a maximally entangled pure state. In qubit-qutrit case, pure states do not exist in the convex set. We construct mixed extreme states of ranks 2 and 3. Second rank extremal state is entangled whereas third rank extreme element is separable. Parthasarathy obtained an upper bound on the rank of extreme states of such a convex set of a bipartite system of n and m dimensions as n2+m2−1. Thus for a qubit-qutrit system, the rank of an extreme element should be less than 12. Since Parthasarathy's bound for two qubit system is 7 and all extreme elements are of rank one, Rudolph posed a question about its tightness. We establish that Parthasarathy's upper bound is tight for qubit-qutrit system.
A Blockchain Based Privacy-Preserving Cloud Service Level Agreement Auditing Scheme

Cloud computing can provide on-demand resource services for customers, but also faces server downtime and security issues. The cloud Service Level Agreement (SLA), as a compensation agreement between customers and service providers, has some problems such as non active execution, disputes and infringement. Existing SLA monitoring solutions either lack multi-party trust, have weak audit ability, or have privacy issue. To address the above problems, a blockchain-based cloud SLA violation monitoring and auditing model is proposed. This model provides multi-party trust through blockchain, ensures data authenticity by a dual monitoring method and keeps monitoring result securely stored on the blockchain by appling trapdoor order revealing encryption algorithm (TORE). Besides, a smart contract is designed to automatically perform auditing tasks to ensure credible violation judgment and privacy preserving. At last, some punishment strategies for violations are introduced, which can distribute compensation or change user’s reputation with smart contracts. With experiments, we exam the cost of the blockchain-based system and demonstrate the feasibility of our proposed model.
Decentralized trustless gossip training of deep neural networks

Novel machine learning techniques apply decentralized model training in order to mitigate data volume and privacy issues. Current approaches assume (a) node performance homogeneity, and (b) simultaneous training. These assumptions also imply that the predictive performance of the distributed models evolves uniformly. A different approach is required since a distributed decentralized network is heterogeneous and nonstationary: nodes can join or leave the network at any point in time (churn). We propose a novel protocol for exchanging the model knowledge between peers using a gossip algorithm combined with the stochastic gradient descent (SGD). Our method has the advantage of being fully asynchronous, decentralized, trustless, and independent of the network size and the churn ratio. We validated the proposed algorithm by running network simulations in various scenarios.
Stream Reasoning: From Theory to Practice

Stream Reasoning is set at the confluence of Artificial Intelligence and Stream Processing with the ambitious goal to reason on rapidly changing flows of information. The goals of the lecture are threefold: (1) Introducing students to the state-of-the-art of Stream Reasoning, (2) Deep diving into RDF Stream Processing by outlining how to design, develop and deploy a stream reasoning application, and (3) Jointly discussing the limits of the state-of-the-art and the current challenges.
Long Short Term Memory Convolutional Neural Network for Indonesian Sentiment Analysis towards Touristic Destination Reviews

Large amount of text has been created on the Internet which requires assessment to convert this data into useful information. Deep learning can address this challenge by delivering improved performance in sentiment analysis compared to classic machine learning that utilises the statistical technique. LSTM (Long short-term memory), CNN (Convolutional neural network), their combined model, and developments in their architecture have shown excellent performance for assessment of sentiment in English corpus. However, there have been limited research works on deep learning that utilizes a blend of the two models for the Indonesian body of languages. In this research, we present the LSTM-CNN combined model and the Word2Vec framework for assessment of sentiment in the Indonesian language with respect to the reviews of tourist regions. The dataset comprises 10000 touristic destination reviews in the Indonesian language (5000 positive and 5000 negative reviews). The parameters for LSTM-CNN and Word2Vec which were put to test in the study are dropout, pooling layer, learning level, convolutional activation, Word2Vec architecture, Word2Vec evaluation approach, and Word2Vec dimension. The outcomes indicate that the precision of the LSTM-CNN model is higher compared to LSTM; the precision of LSTM-CNN is 97.17% as against 90.82% for LSTM. Going forward, our results could be utilised by the government or the tourism sector as a material basis for fostering tourism, and by the public as a platform for selecting travel destination.
Good vs. Evil

While smart home devices are spreading rapidly, the privacy and security of users are key concerns. Many users struggle in acquiring and applying security recommendations to protect against malicious behavior in smart home systems, which can cause users to lose interest in this topic. Game-based learning is a powerful practice to increase the motivation of users in an entertaining and intuitive way. In this paper, we explore the effect of game premise on user's motivation and performance in an educational game. We designed a game with the aim to enlighten users about smart home security challenges. We developed two versions of the game with opposing game premises, a good and an evil, and compared them in a between-group experiment. The results show high motivation ratings in both versions of the game towards solving smart home security problems. However, there are no significant differences between the opposing game premises.
Missing Data Imputation Using Data Generated By GAN

Missing data is a common and challenging problem that arises in many research domains and led to the complication of data analysis. Therefore, handling missing data is a necessity as proposed in many previous studies. In this paper, we proposed two methods to impute missing numerical datasets based on generated data by GAN and determine the imputed values using Euclidian distance. In various missing percentages, we evaluated the imputation accuracy of all methods using MAE and RMSE tests. The proposed methods randomGAN and meshGAN produce the best imputation accuracy in 2 out of 4 datasets against three compared methods: SimpleImputer, IterativeImputer, and KNNimputer.
Research and Implementation of the HD Video Real-Time Edge Detection System Based on FPGA

Abstract In this paper, we propose a high-definition (HD) video real-time edge detection system, also presenting its hardware design and implementation results. Traditional median filtering blurs image details while removing noise, therefore we propose the improved median filtering algorithm which not only effectively removes impulse noise, but also retains more image details. For the problem that traditional sobel edge detection algorithm easily loses graphic details. This paper proposes the improved sobel edge detection algorithm which extends the gradient direction to eight, introducing local adaptive threshold and non-maximum suppression. The proposed algorithms were designed with Verilog-HDL, and the real-time operation was verified and evaluated using an field programmable gate array (FPGA) based test system at an operating frequency of 200MHz for 1280×720 HD video.
Bi-directional Long Short-Term Memory Networks for Fall Detection using Bioradars

At present fall episodes present a huge threat to the health and life of the elderly. That is why a lack of effective and easy-to-use methods for automatic fall detection is a great problem of modern geriatrics. The present paper presents a method for unobtrusive fall detection of elderly based on a bioradar signal analysis. The method was tested using data of six volunteers performing different movement patterns in realistic surrounding conditions. The proposed long short-term memory network classifier proved to be applicable for detecting falls with an accuracy and Fl-score of 99 %.
Weighted Local Feature Vehicle Re-identification Network

With the rapid development of science and technology, how to accurately identify the same vehicle under different cameras is of great significance to smart city construction. At present, most of the vehicle re-identification methods only use global features, and often neglect the local features that often play an important role in it. To overcome this problem, we propose a multi-scale feature network with an attention module to integrate global and local features. Multi-scale feature fusion to reduce the loss of information caused by network deepening obtained more feature information, and enables the network to learn multi-level feature information. The attention module can make the network pay more attention to the discriminative features of the vehicle, such as windshield stickers and scratches on the vehicle. At the same time, we weighted the local features considerations. Extensive experiments demonstrate the effectiveness of our approach and we achieve state-of-the-art results on two challenging datasets, including VeRi-776 [1-3] and VRIC [4].
Size-based Feature Extraction on Blood Cells Calculation Process Using k-Means Clustering

In human blood, there is a blood plasma that has the most important function in the human body. Blood Plasma consists of red blood cells (erythrocytes), white blood cells (leukocytes), and platelets. Of these three blood cells can be known health condition in humans, by counting the number of blood cells contained in the blood. In this modern era, computing systems are very helpful for various areas, one in the medical field to calculate and classify the shape and size of cells in the blood. Therefore, this research creates a system that is capable of extraction of features on blood cells using morphological methods as well as grouping of multiple blood cells based on size using the method K-Means Clustering to calculate the number of Blood cells. The image processing patterns in blood cells begins with the image capture of blood cells using a microscope, the image of blood cells in the extraction based on size and shape using morphological surgery, then from the process of extraction of blood cells will be in Group into three based on the size and shape of the erythrocytes, leukocytes, and platelets using K-Means Clustering.The processing of image patterns in blood cells begins with the image capture of blood cells using a microscope, the image of blood cells in the extraction based on size and shape using morphological surgery, the results of the extraction process of blood cells will be grouped into three based on size and shape using K-Means Clustering. The grouping process will be known to the number of each of these blood cells. The method used in this study resulted in the percentage of successful object recognition and a very good calculation. Results of the percentage generated 60% success and error 40%, it is still a lot of improvement with the process of object recognition based on the characteristics of the blood cells using other methods.
Experimental Analysis Design of Solar Panel Energy Monitoring Prototype

Nowadays, the use of renewable energy is most discusses in energy field issues. The relation about a lack of mineral energy resources and many sources of pollutants fueled by oil and gas became a cause of it. The use of renewable energy with solar sources is a topic that considers discussing. These reasons encourage the authors to initiate research about the use of solar panels in the laboratory as a teaching aid. In this laboratory-based experiment analysis, we can use halogen lamps instead of sunlight. Energy monitoring systems using controllers in the form of PLCs and microcontrollers are several possible options. The use of this control system allows us to monitor energy regularly. In this paper, the authors use a microcontroller as a control system. Instead, the LoRa system is also equipped in this experiment to support the acquisition of data generated remotely. Besides, we can determine the best angle position to produce energy from renewable energy sources. This experimental analysis design of solar panel energy monitoring prototype paper is an initial and global discussion about solar panel energy monitoring design. This paper shows some of the current measurement and angle tilt effect on design.
Analysis and correction of spherical aberrations in long focal length measurements

In this paper, a highly accurate correction method for errors caused by spherical aberrations in long focal length measurements based on divergent light and Talbot interferometry is presented. Simulations and analysis indicate that spherical aberrations are key factors affecting the precision of moiré fringe angles, which are ignored in most methods yet. Moiré fringes are transformed into frequency domains, and a new method based on weighted averages is proposed to improve the precision of measurements. The modified measurement results of 13,500 mm focal length lens reveal that the relative accuracy is better than 0.007%. Comparing the modified relative accuracy with that obtained in interferometry measurement, the proposed method is proved to be highly effective and reliable.
Extracting and Structuring Latent Knowledge for Risk Recognition from Eyes and Utterances of Field Overseers

A tool for promoting verbalization of latent field overseers’ knowledge for risk recognition with eyes and an algorithm for visualizing logic structure in risk knowledge as propositional network are proposed in the paper. The tool was applied to eliciting risk knowledge from actual field overseers and the algorithm was also applied to visualizing logical structures of risk knowledge included in their utterances. As a result, we find out noticeable structure in experts’ knowledge for risk recognition and significant differences between risk knowledge used by the field overseers and knowledge described in work procedure manual.
Time Minimization of Rescue Action Realized by an Autonomous Vehicle

In rescue operations, the full time of action plays important role. It is the sum of the planning, travel, and manipulation (in the action place) phase times. The time minimization of the first two phases by autonomous vehicle for remote action is considered in the paper. For a known a priori map, the path planning consists of local optimal decisions collected next in the general algorithm of the optimal path. Such an approach significantly reduces time of path planning. The robot features and known sparse obstacles reduce the allowable robot speeds. The time of travel is calculated from an allowable velocity profile. Therefore, it can be used to estimate the travel performance. Genetic algorithm and random search-based methods for path finding with travel time optimization are used and compared in the paper. All the proposed time optimization solutions of rescue operations are checked during computer simulations, and results of the simulations are presented.
Conventional Biometrics and Hidden Biometric: A Comparative Study

The biometric verification is necessary for the identification of human subjects for the purpose of security and authentication. Biometric data are very much unambiguous to each one, easily obtainable non-intrusively, no significant changes over a period of time and detectable by humans without much special training. Biometric traits provide a unique natural signature of an individual and it is accepted everywhere. Each biometric technique has its advantage and disadvantage. The methods based on conventional biometric traits such as the face, fingerprint, iris, signature, speech, etc., face problems of theft, imposter, and other security problems. Biometric template once theft cannot be used again. Hidden biometrics is a field where structural features of hidden body parts are used for biometric identification of human subjects. In this chapter, we will show a comparison of different hidden biometrics present and can be used for authentication.
Attention Mechanism for Fashion Image Captioning

Image captioning tries to make computer learn to understand the visual content of a given image and produce one or more description sentences. Recently, with the help from rapid development of deep learning, image captioning has become an active research. The encoder-decoder architecture is commonly considered as the baseline method to address image captioning. This model focuses on recognizing objects and the relationship among these objects. However, with fashion images, the generated sentence should not only describe items inside the input image, but also mention item attributes such as texture, fabric, shape, style, etc. This requirement of fashion image captioning is not able to be solved by the image-captioning method based on the traditional encoder-decoder architecture. Our study addresses this issue by proposing an image captioning model based on the attention mechanism for fashion image which is able to cover both items and the relationship among the detailed attributes of items. We introduce an efficient framework for fashion image captioning that incorporates spatial attention inside the traditional encoder-decoder architecture. Our model generates fashion image captions using the spatial attention mechanism, which dynamically modulates the sentence generation context in multi-layer feature maps. Experiments were conducted on Fashion-Gen, one of state-of-the-art fashion image dataset, and achieved CIDEr/ROUGE-L/BLEU-4 scores of 0.913, 0.502, and 0.221, respectively. Based on experiments, it is consistently presented that our proposed model has significantly improved the performance of fashion-image captioning task, and it even surpasses the baseline methods on the same fashion benchmark dataset.
Scoring High

Large-scale events pose severe challenges to live video streaming service providers, who need to cope with high, peaking viewer numbers and the resulting fluctuating resource demands, keeping high levels of Quality of Experience (QoE) to avoid end-user frustration and churn. In this paper, we analyze a unique dataset consisting of more than a million 2018 FIFA World Cup mobile live streaming sessions, collected at a large national public broadcaster. Different from previous work, we analyze QoE and user engagement as well as their interaction, in dependency to specific soccer match events, which have the potential to trigger flash crowds during a match. Flash crowds are a particular challenge to video service providers, since they cause sudden load peaks and consequently, the likelihood of quality problems. We further exploit the data to model viewer engagement over the course of a soccer match, and show that client counts follow very similar patterns of change across all matches. We believe that the analysis as well as the resulting models are valuable sources of insight for service providers, equipping them with tools for customer-centric resource and capacity management.
Overcoming Stealthy Adversarial Attacks on Power Grid Load Predictions Through Dynamic Data Repair

For power distribution networks with connected smart meters, current advances in machine learning enable the service provider to utilize data flows from smart meters for load forecasting using deep neural networks. However, recent research shows that current machine learning algorithms for power systems can be vulnerable to adversarial attacks, which are small designed perturbations crafted on normal inputs that can greatly affect the overall performance of the predictor. Even with only a partial compromise of the network, an attacker could intercept and adversarially modify data from some smart meters in a limited range to make the load predictor deviate from normal prediction results. In this paper, we leverage the dynamic data-driven applications systems (DDDAS) paradigm and propose a novel data repair framework to defend against these kinds of adversarial attacks. This framework complements the predictor with a self-representative auto-encoder and works in an iterative manner. The auto-encoder is used to detect and reconstruct the likely adversarial part in the input data. Different reconstruction results come up given different sensitivity levels in detection. As new data flows in each iterative time step, the service provider continuously checks the error of the previous prediction step and dynamically trades off between different detection sensitivity levels to seek an overall stable data reconstruction. Case studies on power network load forecast regression demonstrate the vulnerability of current machine learning algorithms and correspondingly the effectiveness of our defense framework.
Maximizing the reduction ability for near-maximum independent set computation

Finding the maximum independent set is a fundamental NP-hard problem in graph theory. Recent studies have paid much attention to designing efficient algorithms that find a maximal independent set of good quality (the more vertices the better). Kernelization is a widely used technique that applies rich reduction rules to determine the vertices that definitely belong to the maximum independent set. When no reduction rules can be applied anymore, greedy strategies including vertex addition or vertex deletion are employed to break the tie. It remains an open problem that how to apply these reduction rules and determine the greedy strategy to optimize the overall performance including both solution quality and time efficiency. Thus we propose a scheduling framework that dynamically determines the reduction rules and greedy strategies rather than applying them in a fixed order. As an important reduction rule, degree-two reduction exhibits powerful pruning ability but suffers from high time complexity O ( nm ), where n and m denote the number of vertices and edges respectively. We propose a novel data structure called representative graph, based on which the worst-case time complexity of degree-two reduction is reduced to O ( m log n ). Moreover, we enrich the naive vertex addition strategy by considering the graph topology and develop efficient methods (active vertex index and lazy update mechanism) to improve the time efficiency. Extensive experiments are conducted on both large real networks and various types of synthetic graphs to confirm the effectiveness, efficiency and robustness of our algorithms.