Reviewerly/exHarmony huggingface dataset:
FamilySong

FamilySong aims to connect internationally distributed family groups via synchronized music-listening. It fosters feelings of togetherness and mutual belonging, that is, connection and culture. We conceptualize it as a domestic Media Space with no live audio or video. It emphasizes intimacy without intrusion, and lives within an existing ecology of interactive technologies. The design journey includes both autobiographical and research-through-design components with similarly-structured family groups (very young children with parents located in the United States and grandparents in Ecuador). Parents, children and grandparents all participated both in the moment and in subsequent interaction grounded in the FamilySong experience. Grandparents took the lead in expressing the importance of the values and goals they saw embedded in the system. This work presents a design that illuminates what it means to connect like a family in which values, needs and priorities are interdependent, and joy and delight are important.
Manipulation of eight-dimensional Bell-like states

High-dimensional Bell-like states are necessary for increasing the channel capacity of the quantum protocol. However, their preparation and measurement are still huge challenges, especially for the latter. Here, we prepare an initial eight-dimensional Bell-like state based on hyperentanglement of spin and orbital angular momentum (OAM) of the first and the third orders. We design simple unitary operations to produce eight Bell-like states, which can be distinguished completely in theory among each other. We propose and illustrate a multiple projective measurement scheme composed of only linear optical elements and experimentally demonstrate that all the eight hyperentangled Bell-like states can be completely distinguished by our scheme. Our idea of manipulating the eight Bell-like states is beneficial to achieve the 3-bit channel capacity of quantum protocol, opening the door for extending applications of OAM states in future quantum information technology.
Consensus mechanisms and information security technologies

Distributed Ledger Technology (DLT) helps maintain and distribute predefined types of information and data in a decentralized manner. It removes the reliance on a third-party intermediary, while securing information exchange and creating shared truth via transaction records that are hard to tamper with. The successful operation of DLT stems largely from two computer science technologies: consensus mechanisms and information security protocols. Consensus mechanisms, such as Proof of Work (PoW) and Raft, ensure that the DLT network collectively agrees on contents stored in the ledger. Information security protocols, such as encryption and hashing, protect data integrity and safeguard data against unauthorized access. The most popular incarnation of DLT has been used in cryptocurrencies, such as Bitcoin and Ethereum, through public blockchains, which requires the application of more robust consensus protocols across the entire network. An example is PoW, which has been employed by Bitcoin, but which is also highly energy inefficient. Other forms of DLT include consortium and private blockchains where networks are configured within federated entities or a single organization, in which case less energy intensive consensus protocols (such as Raft) would suffice. This chapter surveys existing consensus mechanisms and information security technologies used in DLT.
A novel approach for automatic remodularization of software systems using extended ant colony optimization algorithm

Software modularization is extremely important to streamline the inner structure of the program modules without influencing its core functionality. As the framework advances during the upkeep stage, the pristine design of the software package gets disintegrated and hence it is arduous to understand and maintain. There are many existing approaches being carried out to automatically remodularize using optimization techniques to ease the maintenance and improve the quality of the system. The outcomes are rather insufficiently optimal and depend on problem-specific operators, which in turn expands the time multifaceted nature to land at an answer. Apart from these limitations, the issues, such as time complexity, scalability and performance need to be addressed. In this paper, an efficient automatic software remodularization using extended Ant Colony Optimization (ACO) has been proposed to remodularize the software systems. The proposed approach mainly includes two phases: optimised traversal of software system using ACO for finding the order of software files to be processed and remodularization of software system using the proposed approach of extended ACO. We experimented our proposed approach on seven software systems. The performance is evaluated by using Turbo modularization quality (MQ) which supports Module dependency graph (MDG) that have edge weights. The time complexity of remodularized software system is evaluated based on number of Turbo MQ. It can be concluded that when the performance has been compared with the subsisting methodologies, for example, Genetic algorithm (GA), Hill climbing (HC) and Interactive genetic algorithms (I-GAs), the proposed approach has higher Turbo MQ value with lesser time complexity in the evaluated software systems.
A bounded actor–critic reinforcement learning algorithm applied to airline revenue management

Reinforcement Learning (RL) is an artificial intelligence technique used to solve Markov and semi-Markov decision processes. Actor critics form a major class of RL algorithms that suffer from a critical deficiency, which is that the values of the so-called actor in these algorithms can become very large causing computer overflow. In practice, hence, one has to artificially constrain these values, via a projection, and at times further use temperature-reduction tuning parameters in the popular Boltzmann action-selection schemes to make the algorithm deliver acceptable results. This artificial bounding and temperature reduction, however, do not allow for full exploration of the state space, which often leads to sub-optimal solutions on large-scale problems. We propose a new actor–critic algorithm in which (i) the actor’s values remain bounded without any projection and (ii) no temperature-reduction tuning parameter is needed. The algorithm also represents a significant improvement over a recent version in the literature, where although the values remain bounded they usually become very large in magnitude, necessitating the use of a temperature-reduction parameter. Our new algorithm is tested on an important problem in an area of management science known as airline revenue management, where the state-space is very large. The algorithm delivers encouraging computational behavior, outperforming a well-known industrial heuristic called EMSR-b on industrial data.
Energy Efficient Indivisible Workload Distribution in Geographically Distributed Data Centers

In this paper, we investigate the problem of energy cost minimization for geographically distributed data centers with the guaranteed quality of service (i.e., service delay) under time-varying system dynamics.In order to satisfy the user demands, these data centers (DCs) consume a large amount of energy.The increasing energy cost of the DCs is a contemporary problem for the online service providers.To reduce the energy cost of the DCs, recent research studies suggest the workload distribution techniques among geo-distributed data centers by exploiting the dynamic electricity prices and an increased use of the renewable energy.In this paper, we propose a green geographical load balancing (GreenGLB) online algorithm based on the greedy algorithm design technique for the interactive and indivisible workload distribution.An indivisible workload is a sequential task, which cannot be further divided and must be assigned to a single data center.The basic idea of our algorithm is to assign the incoming workload at each time considering the current offered prices of electricity, the renewable energy levels, and respecting the given set of constraints.The experimental results based on the real-world traces illustrate the effectiveness of GreenGLB over the existing workload distribution techniques and attain a significant reduction in the energy cost of the geo-distributed DCs.
High-temperature deformation measurement of the heated front surface of hypersonic aircraft component at 1200 °C using digital image correlation

The outside surface of a hypersonic aircraft flying in the atmosphere is in an extremely harsh high-temperature environment due to severe aerodynamic heating. It is important and difficult to measure the deformation of the heated front surfaces of aircraft components in high-temperature aerobic environments in transient aerodynamic heating experimental simulation system for high-speed aircraft. In this paper, an experimental system of strain measurement for the heated front surfaces of hypersonic aircraft components was established. The strain distribution of the front surface at temperatures up to 1200°C in an aerobic environment was successfully obtained in combination with a digital image correlation method. The processing results of speckle images of heated front surfaces of two different materials show that the measurement results for Al2O3 ceramics have a high goodness of fit with the "strain-temperature" relationship derived based on Hillman's "thermal expansion coefficient-temperature" relationship. The experimental results of the nickel-based superalloy (1Cr18Ni9Ti) conform well with existing data, which verified the credibility and effectiveness of the proposed experimental method. The optical measurement system developed for measuring strain on the heated front surfaces of components in a 1200°C high-temperature environment provides an important means for thermal strength analysis as well as for the safety and reliability design of the heated components of hypersonic aircraft.
Impact of Fuzziness Measures on the Performance of Semi-supervised Learning

Usage of fuzziness in the study of semi-supervised learning is relatively new. In this study, the divide-and-conquer strategy is used to investigate the performance of semi-supervised learning. To this end, testing dataset is divided into three categories, namely low, medium and high-fuzzy samples based on the magnitude of fuzziness of each sample. It is experimentally confirmed that if the low-fuzzy samples are added from the testing dataset to the original training dataset and the model is retrained, then the accuracy can be improved. To measure the amount of fuzziness of each sample, four different fuzziness measuring models are used in this study. Experimental results support that improvement of accuracy is dependent on which fuzziness measuring model is used to measure the fuzziness of each sample. Wilcoxon signed-rank test shows that choosing a specific fuzziness measuring model is significant or not. Finally, from the Wilcoxon signed-rank test, the best model is chosen, which can be used along with semi-supervised learning to improve its performance.
Credit Card Fraud Identification Using Machine Learning Approaches

Due to rapid growth of internet, online buying product is an important part of everyone's lifestyle most of the time MasterCard is employed to pay online for products. It's a straightforward thanks to looking, people will get their required product on your visual display unit or on sensible phone. For online purchase use of MasterCard will increase dramatically however still there's some loop holes in system of online looking that causes online frauds or credit card frauds. Thus, fraud detection systems became essential for all MasterCard supply banks to attenuate their losses. The foremost normally used fraud detection strategies are Neural Network (NN), rule-induction techniques, fuzzy system, call trees, Support Vector Machines (SVM), Logistic Regression, Local Outlier Factor (LOF), Isolation Forest, K-Nearest Neighbor, Genetic algorithms. These techniques are often used alone or unitedly mistreatment ensemble or meta-learning techniques to make classifiers. This paper presents a survey of various techniques utilized in MasterCard fraud detection and evaluates every methodology supported bound criterion.
Interpretable Question Answering on Knowledge Bases and Text

Interpretability of machine learning (ML) models becomes more relevant with their increasing adoption.In this work, we address the interpretability of ML based question answering (QA) models on a combination of knowledge bases (KB) and text documents.We adapt post hoc explanation methods such as LIME and input perturbation (IP) and compare them with the self-explanatory attention mechanism of the model.For this purpose, we propose an automatic evaluation paradigm for explanation methods in the context of QA.We also conduct a study with human annotators to evaluate whether explanations help them identify better QA models.Our results suggest that IP provides better explanations than LIME or attention, according to both automatic and human evaluation.We obtain the same ranking of methods in both experiments, which supports the validity of our automatic evaluation paradigm.
A High Capacity Geometrical Domain Based 3D Image Steganography Scheme

As a result of the tremendous advances in digital communications and media, 3D models have become the core of various applications in diverse fields. Consequently, these models can be utilized in steganography acting as secure and innocent host media for sheltering covert data. In this paper, Blowfish encryption algorithm is performed on the secret data for a higher security level. Then, a geometrical domain based steganography method is implemented, where the polygons of triangular meshes of various Stanford 3D models are slightly modified so that they hold the secret data. The proposed method is found robust against geometrical attacks on 3D models such as rotation, uniform scaling and translation. Furthermore, it has the capability to reveal the secret data blindly. The performance of this proposed algorithm is measured and compared to its counterparts from the literature. The numerical results are a superb indicator for a reliable high capacity message security scheme.
Spectrum-Based Hand Gesture Recognition Using Millimeter-Wave Radar Parameter Measurements

Radar sensors offer several advantages over optical sensors in the gesture recognition for remote control of electronic devices.In this paper, we investigate the feasibility of human gesture recognition using the spectra of radar measurement parameters.With the combination of radar theory and classification methods, we found that the frequencies of different gestures' parameters could be utilized as features for gesture recognition.Six kinds of periodic dynamic gestures are designed to avoid the complexity of defining and extracting the start and end of the dynamic gesture.In addition to the frequency ratio, we also extracted some features related to motion range and detection coherence to eliminate the interferences brought by the unintended gestures.The decision tree classifier designed on the basis of experimental phenomena can guarantee effective classification between different gestures, and in general, the correct recognition rate of each gesture is higher than 90%.Finally, we collected the position and the Doppler velocity information of hand for classification by a W-band millimeter wave radar in the experiment and verified the usability of the proposed method.
Deceptive Reviews Detection Using Deep Learning Techniques

With the increasing influence of online reviews in shaping customer decision-making and purchasing behavior, many unscrupulous businesses have a vested interest in generating and posting deceptive reviews. Deceptive reviews are fictitious reviews written deliberately to sound authentic and deceive the consumers. Traditional deceptive reviews detection methods are based on various handcrafted features, including linguistic and psychological, which characterize the deceptive reviews. However, the proposed deep learning methods have better self-adaptability to extract the desired features implicitly and outperform all traditional methods. We have proposed multiple Deep Neural Network (DNN) based approaches for deceptive reviews detection and have compared the performances of these models on multiple benchmark datasets. Additionally, we have identified a common problem of handling the variable lengths of these reviews. We have proposed two different methods – Multi-Instance Learning and Hierarchical architecture to handle the variable length review texts. Experimental results on multiple benchmark datasets of deceptive reviews have outperformed existing state-of-the-art. We evaluated the performance of the proposed method on other review-related task-like review sentiment detection as well and achieved state-of-the-art accuracies on two benchmark datasets for the same.
Decentralized ciphertext-policy attribute-based encryption schemes for lightweight devices

In a traditional attribute-based encryption (ABE) system, there is only one central authority who generates and hence knows the secret keys of all users, this problem is known as the key escrow problem. An ABE scheme which can deal with the aforementioned problem is so-called a decentralized (or multi-authority) ABE scheme. There have been several efforts to propose decentralized ABE schemes in the literature, and these schemes can enjoy some interesting properties such as fine-grained access control and full security. However, all of them suffer from two main weaknesses that are large secret key size and slow decryption time. These weaknesses are obviously not desirable for lightweight device applications such as Internet of Things applications. In this paper, we propose two decentralized ciphertext-policy attribute-based encryption schemes. Our proposed schemes have some nice properties such as constant-size secret key, fast decryption, optimized ciphertext size and fine-grained access control. Our schemes, as a result, could be seen as a solution for lightweight device applications. For completeness, we implement our schemes and give some concrete benchmarks.
Automatic HTML Code Generation from Mock-Up Images Using Machine Learning Techniques

The design cycle for a web site starts with creating mock-ups for individual web pages either by hand or using graphic design and specialized mock-up creation tools. The mockup is then converted into structured HTML or similar markup code by software engineers. This process is usually repeated many more times until the desired template is created. In this study, our aim is to automate the code generation process from hand-drawn mock-ups. Hand drawn mock-ups are processed using computer vision techniques and subsequently some deep learning methods are used to implement the proposed system. Our system achieves 96% method accuracy and 73% validation accuracy.
An approach for augmented learning of finite element analysis

Abstract Finite element analysis (FEA) is taught in most engineering faculties due to its extensive applications. However, the pedagogy is restricted due to the lack of effective and efficient educational tools. Besides plowing through tedious theories, students have to overcome challenges of using commercial FEA software for problem solving, which often has nonintuitive user interfaces. To help students grasp FEA easily without a stiff learning curve in understanding the software, this paper proposes to integrate the Augmented Reality (AR) technology with FEA. With such integration, FEA results can be augmented and visualized on physical structures to facilitate understanding and interpreting FEA results. In addition, students can perform various FEA tasks directly on physical structures, such as applying loads to investigate structural behavior, exploring FEA results through slicing and clipping, and so forth. A prototype system is built and a user study is conducted. The results have demonstrated the unique features of this novel approach.
ForestLayer: Efficient training of deep forests on distributed task-parallel platforms

Most of the existing deep models are deep neural networks. Recently, the deep forest opens a door towards an alternative to deep neural networks for many tasks and has attracted more and more attention. At the same time, the deep forest model becomes widely used in many real-world applications. However, the existing deep forest system is inefficient and lacks scalability. In this paper, we present ForestLayer, which is an efficient and scalable deep forest system built on distributed task-parallel platforms. First, to improve the computing concurrency and reduce the communication overhead, we propose a fine-grained sub-forest based task-parallel algorithm. Next, we design a novel task splitting mechanism to reduce the training time without decreasing the accuracy of the original method. To further improve the performance of ForestLayer, we propose three system-level optimization techniques, including lazy scan, pre-pooling, and partial transmission. Besides the systematic optimization, we also propose a set of high-level programming APIs to improve the ease-of-use of ForestLayer. Finally, we have implemented ForestLayer on the distributed task-parallel platform Ray. The experimental results reveal that ForestLayer outperforms the existing deep forest system gcForest with 7× to 20.9× speedup on a range of datasets. In addition, ForestLayer outperforms TensorFlow-based implementation on most of the datasets, while achieving better predictive performance. Furthermore, ForestLayer achieves good scalability and load balance.
Model-Extraction Attack Against FPGA-DNN Accelerator Utilizing Correlation Electromagnetic Analysis

This work presents a model-extraction attack on a DNN accelerator, which is implemented on FPGA. An adversary can get DNN model parameters by exploiting electromagnetic leakage from the accelerator during operation. Our experimental results show that the adversary can extract trained model parameters from a DNN accelerator even if the DNN model parameters are protected with data encryption. This suggests that countermeasures against side-channel leaks are important when implementing a DNN accelerator on FPGA or ASIC.
Realtime Face-Detection and Emotion Recognition Using MTCNN and miniShuffleNet V2

In this paper, the problem of facial expression is addressed, which contains two different stages: 1. Face detection, 2. Emotion Recognition. For the first stage, an MTCNN (Multi-Task Convolutional Neural Network) has been employed to accurately detect the boundaries of the face, with minimum residual margins. The second stage, leverages a ShuffleNet V2 architecture which can tradeoff between the accuracy and the speed of model running, based on the users' conditions. The experimental results clearly Shows that our proposed model outperforms the state-of-the-art on FER 2013 dataset which has been provided by Kaggle.
Organization, atmosphere, and digital technologies: Designing sensory order

We argue technology and organization are inherently spatial phenomenon. We conceptualize this conjunction as atmosphere: a gathering of mood, human practice, material and environmental conditions, and values that has sufficient coherence and distinction to constitute a distinct interior. Atmospheres, however, are not entirely stable and present: the interior is porous to outside influence, and the interior is never wholly ordered. We show this through the study of digitally mediated architectural design practice. We find the technological mediation of atmospheres is constituted in sensory and affective spatial arrangements, and not in rationally calculated configurations of assets and goals. An atmosphere is inherently aesthetic. This allows us to gesture toward a definition of organization as technologically mediated spatial struggle to reconcile interior coherence with outward exposure.