Reviewerly/exHarmony huggingface dataset:
Mobile App for Word Recognition and Visualization of Objects Using Indonesian Language Google Speech To Text for Deaf Students

Disability is a condition of many people's limitations throughout the world, including in Indonesia. According to research, deaf sufferers are the most disabled disabilities in Indonesia, including deaf sufferers who are still as school students. Basic learning how to communicate in word and object recognition between teacher and deaf students is a problem that must be solved to encourage deaf students to communicate well. Several studies conducted previously have succeeded in producing a variety of Android-based applications that help deaf in learning. Otherwise, apps that used English and the absence of visualization of an object make students with hearing impairment at the elementary school level find it difficult to understand the form. This research builds a mobile speech to text application and object visualization by utilizing Google speech to text in Indonesian, which is useful in translating sound into text accompanied by display of objects and their colors. Students, teachers, and parents can use applications as alternative learning in addition to conventional methods that are fun for deaf students so that they will be more enthusiastic in learning.
Extraction of Sequence from Bangla Handwritten Numerals and Recognition Using LSTM

In the promising era of Handwritten Numeral Recognition (HNR), despite Bangla being one of the major languages in the Indian subcontinent, fewer explorations have been done on Bangla numerals compared to other languages. Among the existing methods, several convolutional neural network (CNN) based method outperformed other methods. But CNN always gets confused with some specific Bangla numerals due to the similarity of shape and size of different numerals. The main purpose of this study is to expand Bangla HNR by considering a novel methodology with a Long Short-Term Memory (LSTM) network. In the proposed method, images are thinned and a sequence is extracted. These extracted sequences are used to classify using LSTM network. Both single-layer LSTM and Deep LSTM models are trained and performance tested on a benchmark dataset with a large number of samples. On the other hand, traditional CNN is also trained for better understanding. Experimental outcomes revealed that the proposed LSTM based method outperformed CNN with remarkable accuracy for the similar shaped numerals. Finally, the proposed method achieved a test set recognition rate of 98.03% which is better than or competitive to other prominent existing methods.
Wavelet-Based Hybrid Audio Watermarking Using Statistical Mean Manipulation and Spread Spectrum

In this paper, we propose a Discrete Wavelet Transform (DWT) - based hybrid audio watermarking using Statistical Mean Manipulation (SMM) and Spread Spectrum (SS) technique. The host audio is decomposed by DWT to produce the signal in low frequency subband and high frequency subband, where SMM embeds the watermark into low frequency in the first subband, and SS embeds the watermark into high frequency in the selected subband. The embedding process using the SMM technique is the insertion process by modifying the average of the audio signal in one frame according to the watermark, thus it modifies the audio in the low-frequency subband. The SS technique modulates the watermark before it is embedded into the host audio in the higher selected frequency subband. This combination technique produces the robust watermarking method to the signal processing attack, such as Low Pas Filter (LPF), resampling and audio compression while maintaining high watermarked audio quality and watermark payload.
Missing Value Imputation with MERCS: A Faster Alternative to MissForest

Fundamentally, many problems in Machine Learning are understood as some form of function approximation; given a dataset \(\mathcal {D}\), learn a function \(\mathop {} f_{\textit{\textbf{X}} \rightarrow \textit{\textbf{Y}}}\). However, this overlooks the ubiquitous problem of missing data. E.g., if afterwards an unseen instance has missing input variables, we actually need a function \(f_{\varvec{X'}\rightarrow \textit{\textbf{Y}}}\) with \(\textit{\textbf{X}}' \subset \textit{\textbf{X}}\) to predict its label. Strategies to deal with missing data come in three kinds: naive, probabilistic and iterative. The naive case replaces missing values with a fixed value (e.g. the mean), then uses \(\mathop {} f_{\textit{\textbf{X}} \rightarrow \textit{\textbf{Y}}}\) as if nothing was ever missing. The probabilistic case has a generative model \(\mathcal {M}\) of \(\mathcal {D}\) and uses probabilistic inference to find the most likely value of \(\textit{\textbf{Y}}\), given values for any subset of \(\textit{\textbf{X}}\). The iterative approach consists of a loop: according to some model \(\mathcal {M}\), fill in all the missing values based on the given ones, retrain \(\mathcal {M}\) on the completed data and redo your predictions, until these converge. MissForest is a well-known realization of this idea using Random Forests. In this work, we establish the connection between MissForest and MERCS (a multi-directional generalization of Random Forests). We go on to show that under certain (realistic) conditions where the retraining step in MissForest becomes a bottleneck, MERCS (which is trained only once) offers at-par predictive performance at a fraction of the time cost.
Microcontroller-based PID Temperature Controller Using Genetic Algorithms

In this work, a hardware setup has been designed to control the temperature in a varying environment. For this electronic circuitry, namely, temperature controller, an 89C51 microcontroller, has been fabricated, and for online controlling the parameters Genetic Algorithm based fuzzy controller has been implemented in the Visual-Basic5. A lot of work has been done on the fuzzy controllers as they are being used in the industries. But these controllers are unable to acquire the data and have to rely to a great extent on empirical and heuristic data which in many cases, especially in the varying environment, cannot be objectively elicited. For adaptive learning, a Genetic Algorithm technique has been employed which is modeled on the process of Natural Evolution. For adaptive learning, the environment iterative rule learning (IRL) approach has been utilized. With this approach the knowledge base table is modified after learning the environment.
Wavelet Based Entropy Features for Facial Expression Recognition

Texture features are explored in an active manner for precise classification of facial expressions. In this paper, we inquire into wavelet entropy features obtained from the daubechies (db4), coiflets (coif4) and symlets (sym4) wavelet filters and propose a new novel method to classify seven human facial expressions by employing Viola-Jones face detection algorithm, gamma intensity correction (GIC), discrete wavelet transform (DWT) and cascade forward back-propagation neural network (CFBPNN). Extracted entropy features form discrete wavelet transform (DWT) decomposed sub-bands are feed to CFBPNN for training purpose. We use JAFFE and CK+ face database in our experimental work and observe 97.14% and 96.80% classification accuracy respectively.
Visualization by P-flow: gradient- and feature-based optical flow and vector fields extracted from image analysis

We proposed a method for extracting the optical flow suitable for visualization, pseudo-flow (P-flow), from a natural movie [ Exp. Brain Res. 237 , 3321 ( 2019 ) EXBRAP 0014-4819 10.1007/s00221-019-05674-0 ]. The P-flow algorithm comprises two stages: (1) extraction of a local motion vector field from two successive frames and (2) tracking of vectors between two successive frame pairs. In this study, we show that while P-flow takes a feature (vector) tracking approach, it is also classified as a gradient-based approach that satisfies the brightness constancy constraint. We also incorporate interpolation and a corner detector to address the shortcomings associated with the two approaches.
Intellectual Properties of Artificial Creativity: Dismantling Originality in European’s Legal Framework

The debate on whether a work created by AI should be protected is waging on and divides academics into three groups: (1) the ones who maintain a traditional view, arguing that Intellectual Property was made by humans to regulate humans and the works of human intellect, (2) the ones who advocate a redefinition of the concept of intellectual creation, opposing the first view with a more progressive one and (3) those who consider that the law should find an intermediate solution, as the situation is analogous to the employer/employee context (Gürkaynak et al. 2017). However, the main purpose of this paper is not to participate on the above-mentioned discussion. On the contrary, it is assumed that the matter will soon be resolved and, therefore, other important notions in the Intellectual Property's universe, namely, originality and creativity. The bottom-line question is the following: considering the European framework, can artwork created by AI be regarded as creative and thus, original?
The Elephant in the Background

Tracking users is a ubiquitous practice on the web today. User activity is recorded and analyzed on a large scale to create personalized products, forecast future behavior, and prevent online fraud. While HTTP cookies have been the weapon of choice so far, new and more pervasive techniques such as browser fingerprinting are gaining traction. This paper describes how users can be empowered against fingerprinting by showing them when, how, and who is tracking them. To this end, we conduct a systematic analysis of various fingerprinting tools to create FPMON: a browser extension to measure and rate fingerprinting activity on any website in real-time. With FPMON, we evaluate the 10k most popular websites to i) study the pervasiveness of fingerprinting; ii) review the latest countermeasures; and iii) identify the networks that foster the use of fingerprinting. Our evaluations reveal that i) fingerprinters subvert privacy regulations; ii) they are present on privacy-sensitive websites (insurance, finances, NGOs); and iii) current countermeasures cannot sufficiently protect users. Hence, we publish FPMON as a free browser extension to empower users against this growing threat.
Research on the Model of Command and Decision System for Big Data

With the continuous application of high technology in command and operation, the era of big data in military field has come. Traditional data processing can't satisfy the deep analysis of massive heterogeneous data. Cloud computing provides necessary storage, management and computing solutions for big data analysis. Based on the characteristics of big data, using cloud computing and data mining technology, this paper proposes a command and decision system model based on Hadoop to provide efficient and reliable ad hoc analysis, and finally establishes a big data analysis ecosystem for battle command and decision-making.
Towards an Ontology Network on Human-Computer Interaction

Engineering interactive computer systems is a challenging task that involves concerns related to the human-computer interaction (HCI), such as usability and user experience. HCI is a wide domain, where ontologies are useful instruments for supporting knowledge-related problems. However, HCI ontologies have been built and used in isolation. Ideally, in wide domains, ontologies should not be stand-alone artifacts. They should relate to each other, forming a network of interlinked semantic resources, i.e. an ontology network. Therefore, in this paper we introduce HCI-ON, a Human-Computer Interaction Ontology Network composed of ontologies that we have developed and others found in the literature. HCI-ON organizes and integrates knowledge, serving as a basis to several applications. We also discuss mechanisms to evolve HCI-ON and present some envisioned applications.
Study on Text-based and Voice-based Dialogue Interfaces for Human-Computer Interactions in a Blocks World

We conducted a small scale user study to understand user experiences with two different forms of dialogue interfaces - text-based and voice-based - to interact with a virtual agent in a Blocks World environment while perform tower building tasks. The participants also had the option of using deictic gestures in addition to the speech modality. We identify common types of errors/issues that led to communication failures and share our observations about how users reacted to these issues. We also present survey data that reflects users' evaluations of the dialogue interfaces and their interactions with the virtual agent.
A Framework for Assessing Technology-Mediated IPV

Intimate partner violence (IPV) is a common public health problem that damages many romantic relationships. It is defined as abuse or aggression that occurs between intimate partners with an estimated ⅓ of women worldwide having some experience as the recipient of IPV at some point in their lives. While couple therapists are improving their ability to recognize the presence of IPV in practice, there are still gaps in assessment, especially as it pertains to the role technology plays in facilitating and exacerbating IPV. Given the dangers and damage resulting from IPV, it is imperative for couple therapists to assess broadly and across many contexts for the prevalence of technology-mediated IPV. The purpose of this article is to inform clinical practice by (1) presenting reasons why couple therapists overlook technology-mediated violence, and (2) present strategies for therapists to better address technology-mediated IPV.
Weakly-Supervised Semantic Segmentation With Regional Location Cutting and Dynamic Credible Regions Correction

Weakly-supervised semantic segmentation is a challenging task as it outputs pixel-level predictions from weaker labels. Segmentation with weaker labels is an important research area since it can significantly reduce human annotation efforts by associating high-level semantic to low-level appearance. In this article, we propose a novel Regional location Cutting and Dynamic credible regions Correction (RCDC) approach for weakly-supervised semantic segmentation. Only image-level labels are needed and it can take less time for manual annotation. Starting with the weak localization of classification network, our cutting approach combines the weak coverage with the traditional cutting method to obtain the pseudo-labels of around 50% ground truth. Then, our dynamic credible regions correction approach adjusts the loss function during the training to preserve the regions that have the superior performance of each iteration. It can further enhance the pseudo-labels for better segmentation results. Finally, with the fully-connected CRF and the retraining method, our approach obtains a competitive performance on the PASCAL VOC 2012 dataset.
Decoherence dynamics of entangled quantum states in the XXX central spin model

Maintaining coherence of a qubit is of vital importance for realizing a large-scale quantum computer in practice. In this work, we study the central spin decoherence problem in the XXX central spin model (CSM) and focus on the quantum states with different initial entanglement, namely intra-bath entanglement or system–bath entanglement. We analytically obtain their evolutions of fidelity, entanglement, and quantum coherence. When the initial bath spins constitute an N-particle entangled state (the Greenberger–Horne–Zeilinger-bath or the W-bath), the leading amplitudes of their fidelity evolutions both scale as $$\mathscr {O}(1/N)$$ , which is the same as the case of a fully polarized bath. However, when the central spin is maximally entangled with one of the bath spins, the amplitude scaling of its fidelity evolution declines from $$\mathscr {O}(1/N)$$ to $$\mathscr {O}(1/N^2)$$ . That implies appropriate initial system–bath entanglement is contributive to suppress central spin decoherence. In addition, with the help of system–bath entanglement, we realize quantum coherence-enhanced dynamics for the central spin where the consumption of bath entanglement is shown to play a central role.
Improving Multi-agent Reinforcement Learning with Imperfect Human Knowledge

Multi-agent reinforcement learning has gained great success in many decision-making tasks. However, there are still some challenges such as low efficiency of exploration, significant time consumption, which bring great obstacles for it to be applied in the real world. Incorporating human knowledge into the learning process has been regarded as a promising way to ameliorate these problems. This paper proposes a novel approach to utilize imperfect human knowledge to improve the performance of multi-agent reinforcement learning. We leverage logic rules, which can be seen as a popular form of human knowledge, as part of the action space in reinforcement learning. During the trial-and-error, the value of rules and the original action will be estimated. Logic rules, therefore, can be selected flexibly and efficiently to assist the learning. Moreover, we design a new exploration way, in which rules are preferred to be explored at the early training stage. Finally, we make experimental evaluations and analyses of our approach in challenging StarCraftII micromanagement scenarios. The empirical results show that our approach outperforms the state-of-the-art multi-agent reinforcement learning method, not only in the performance but also in the learning speed.
Graph Attentive Network for Region Recommendation with POI- and ROI-Level Attention

Due to the prevalence of human activity in urban space, recommending ROIs (region-of-interest) to users becomes an important task in social networks. The fundamental problem is how to aggregate users’ preferences over POIs (point-of-interest) to infer the users’ region-level mobility patterns. We emphasize two facts in this paper: (1) there simultaneously exists ROI-level and POI-level implicitness that blurs the users’ underlying preferences; and (2) individual POIs should have non-uniform weights and more importantly, the weights should vary across different users. To address these issues, we contribute a novel solution, namely GANR\(^2\) (Graph Attentive Neural Network for Region Recommendation), based on the recent development of attention network and Neural Graph Collaborative Filtering (NGCF). Specifically, to learn the user preferences over ROIs, we provide a principled neural network model equipped with two attention modules: the POI-level attention module, to select informative POIs of one ROI, and the ROI-level attention module, to learn the ROI preferences. Moreover, we learn the interactions between users and ROIs under the NGCF framework. Extensive experiments on two real-world datasets demonstrate the effectiveness of the proposed framework.
Blockchain-based Multiparty Computation System

Data security and privacy preserving have gradually become key issues in the process of data cooperation. Most existing data multiparty computing solutions rely on trusted third parties, and the data owner loses control of the data, which may easily lead to privacy data leakage. This paper designed a multiparty computation system combined with blockchain and secure multiparty computation technology. This system uses the decentralization of the blockchain to remove dependence on third parties in existing data cooperation system, transfers secure multiparty computation protocol to off-chain execution, and on-chain smart contract is responsible for selecting agent computing nodes fairly and generating computation certificate. This system realizes the safe and fair joint computation of multiparty data without the data owner losing control of their original data.
A moderate interpretation of group privacy illustrated by cases from disaster management

Abstract This article uses cases from disaster management as a springboard for presenting a critique of a right to group privacy in a strong sense. As such, the article challenges the idea of strong group privacy, which holds that there are situations in which the group, and not its members, is the holder of a right to privacy. The paper argues for a moderate interpretation of group privacy, stressing that group privacy is a matter of privacy for the members constituting the group. Although data‐driven knowledge discovery implies profiling by group categorization, this observation does not constitute a reason to introduce a right to group privacy for other purposes than to protect the individual's right to privacy. The article demonstrates preliminary theoretical considerations, which may inform the creation of a framework that protects personal privacy by considering a moderate sense of group privacy suited to tackle privacy challenges implied by data analytics.
An Effective Multi-label Classification Algorithm Based on Hypercube

To solve the problem of multi-label classification, a classification algorithm based on hypercube is proposed. For each label in the training sample set, a minimum hypercube containing all its samples is constructed in the sample space. In classification, the labels of the sample to be classified are determined according to the hypercubes to which the sample belongs. If the sample to be classified is not in any hypercube, the magnification factor of the hypercube containing the sample to be classified is calculated respectively, and the label is determined according to the magnification factor. The algorithm avoids the influence of unbalanced data and has good scalability. The experimental results show that the algorithm has faster training speed and classification efficiency, and has higher classification accuracy. The larger the training set size and the more labels, the more obvious the effect.
Bi-objective Intelligent Task Scheduling for Green Clouds with Deep Learning-based Prediction

The ever-increasing deployment of cloud data centers causes high energy consumption, high cost, and harmful environmental pollution. To solve above problems, cloud service providers are actively exploring to use green cloud data centers (GCDCs) by using green energy. Yet it is challenging to accurately predict the future wind and solar energy before making intelligent task scheduling decisions. In addition, it is difficult to jointly optimize cost and revenue. In this work, to make optimal task scheduling, various types of applications, service level agreements, service rates, task loss probability, electricity prices and green energy in different GCDCs are considered. First, this work employs a long short-term memory network to predict wind and solar energy. Then, it adopts a bi-objective optimization algorithm to achieve a better trade-off between cost and revenue of GCDCs. Finally, it adopts real-world data including workload trace, wind energy, solar energy and electricity prices to demonstrate the effectiveness of the proposed energy prediction and task scheduling methods. It's shown that the proposed methods achieve higher performance than other neural network methods.