Reviewerly/exHarmony huggingface dataset:
Cross-modal Relational Reasoning Network for Visual Question Answering

Visual Question Answering (VQA) is a challenging task that requires a cross-modal understanding of images and questions with relational reasoning leading to the correct answer. To bridge the semantic gap between these two modalities, previous works focus on the word-region alignments of all possible pairs without attending more attention to the corresponding word and object. Treating all pairs equally without consideration of relation consistency hinders the model’s performance. In this paper, to align the relation-consistent pairs and integrate the interpretability of VQA systems, we propose a Cross-modal Relational Reasoning Network (CRRN), to mask the inconsistent attention map and highlight the full latent alignments of corresponding word-region pairs. Specifically, we present two relational masks for inter-modal and intra-modal highlighting, inferring the more and less important words in sentences or regions in images. The attention interrelationship of consistent pairs can be enhanced with the shift of learning focus by masking the unaligned relations. Then, we propose two novel losses ℒ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CMAM</inf> and ℒ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SMAM</inf> with explicit supervision to capture the fine-grained interplay between vision and language. We have conduct thorough experiments to prove the effectiveness and achieve the competitive performance for reaching 61.74% on GQA benchmark.
Frequency Selective Filtering of Graph Signal in Directed Graph Fourier Transform Domain

In this paper, a frequency selective filtering of real-valued graph signal in the directed graph Fourier transform (DGFT) domain is presented. First, the spectral properties of the GDFT are investigated. Then, the eigenvalues of directed Laplacian are divided into real and complex valued sets to constrain the ideal spectral response of graph filter such that real-valued filtered signal can be preserved. Next, lowpass, bandpass and highpass filtering methods of graph signals are studied. Finally, the proposed filtering method is used to reduce the unwanted noise superimposed on temperature data for demonstrating the effectiveness of the proposed method.
Performance Comparison of Spongent and Photon Hashing Algorithms in Ethereum-based Blockchain System

ISO/IEC 29192-5 defines three lightweight cryptography hash function algorithms. Some of the lightweight cryptography based on sponge construction are PHOTON and SPONGENT. Given the importance of the hash functions in blockchain technology and the vulnerability and computational power gaps that affect the performance of the blockchain platform, this research will evaluate the SPONGENT-256 and PHOTON-256 hash algorithms to be implemented on Ethereum-based blockchain systems. The implementation of the SPONGENT-256 and PHOTON-256 hash algorithms on an Ethereum-based blockchain system produces output in the form of addresses, public keys and wallets. The experiment results of the SPONGENT-256 algorithm produces is average 0.0328 seconds, with the user time is 0.0208 seconds and the system time is 0.0118 seconds. This will give significant results if implemented on an Ethereum-based blockchain system with many nodes and data to be generated. However, the SPONGENT-256 algorithm requires a lot of memory for processing with 75%. While the KECCAK-256 algorithm uses less memory from the PHOTON-256 and the SPONGENT-256 algorithms with an average memory usage of 55%. Based on the implementation of the SPONGENT-256 hash algorithm on the Ethereum-based blockchain and analysis of the results, it can be concluded that the SPONGENT-256 algorithm can be used as an alternative hash function algorithm for Ethereum-based blockchain system.
Improving ADABoost Algorithm with Weighted SVM for Imbalanced Data Classification

Recently, different boosting algorithms have been proposed in order to improve the performance of classification for imbalanced data. In this paper, we present an improved ADABoost algorithm, called Im.ADABoost, for imbalanced data including two main improvements: (i) initializing different error weights adapted to the imbalance rate of the datasets; (ii) calculating the confidence weights of the member classifier that is sensitive to the total errors caused on the positive label. Additionally, we combine Im.ADABoost with Weighted-SVM to enhance classification efficiency on imbalanced datasets. Our experimental results show some promising potential of the proposed algorithm.
Breast Cancer Detection Using Image Processing and CNN Algorithm with K-Fold Cross-Validation

Breast cancer is the most well-known kind of disease in lady overall representing 20%, everything being equal. That brought 1.68 million new cases and 522,000 Passings. One of the serious issues is that ladies regularly disregard the indications, which could cause more unfriendly consequences for them accordingly dropping down the endurance potentials. In created nations, the endurance rate is albeit high, yet it is a space of worry in the agricultural nations where the 5-year endurance rates are poor. In India, there are around 1,000,000 cases each year and the five-year endurance of stage IV bosom disease is about 10%. Accordingly, it is vital to recognize the signs as ahead of schedule as could be expected.
Remote Sensing Image Zero Watermarking Algorithm Based on DFT

Abstract This paper proposes a remote sensing image zero-watermark algorithm based on DFT, which can be used to protect the rights and interests of digital image copyright owners. First, the high frequency part after DCT transformation is binarized to obtain the characteristic image. Second, block DFT processing the host image. The high frequency part after DFT and the characteristic image are XORed to complete the watermark embedding. In order to test the performance of the watermark, an attack was carried out on the image embedded with the watermark. In order to extract the watermark from the attacked image, the high-frequency part of the data extracted from the attacked image is XORed again with the result of the first XOR. Compare the extracted watermark with the embedded watermark, and calculate its NC value. Most of the NC values are close to 1, indicating that the algorithm is robust. In addition, the PSNR values of the attacked image and the original image are also calculated. The experimental results show that the watermark obtained by the algorithm in this paper has strong invisibility and robustness.
Speculative taint tracking (STT)

Speculative execution attacks present an enormous security threat, capable of reading arbitrary program data under malicious speculation, and later exfiltrating that data over microarchitectural covert channels. This paper proposes speculative taint tracking (STT), a high security and high performance hardware mechanism to block these attacks. The main idea is that it is safe to execute and selectively forward the results of speculative instructions that read secrets, as long as we can prove that the forwarded results do not reach potential covert channels. The technical core of the paper is a new abstraction to help identify all micro-architectural covert channels, and an architecture to quickly identify when a covert channel is no longer a threat. We further conduct a detailed formal analysis on the scheme in a companion document. When evaluated on SPEC06 workloads, STT incurs 8.5% or 14.5% performance overhead relative to an insecure machine.
Workload Prediction in BTC Blockchain and Application to the Confirmation Time Estimation

Blockchains are distributed ledgers storing data and procedures in an immutable way. The validation of the information stored therein as well as the guarantee of its immutability can be achieved without the need of a central authority. Proof-of-work is the maximum expression of the distributed nature of such systems, and requires miners to spend a large amount of energy to secure the blockchain. The cost is mostly paid by the end-users that offer fees to support the validation of their transactions. In general, higher fees correspond to shorter validation delays. However, given the limited throughput of the system and variability of the workload, the fee one needs to offer to satisfy a certain requirement on the validation delay strongly depends on the intensity of the workload that, in turns, is subject to high variability.In this work, we propose a time series analysis of the workload of Bitcoin blockchain and compare the accuracy of Facebook Prophet model with a ARIMA model. We take into account the periodicity of the workload and show by simulations how these predictions, accompanied with their confidence intervals, can be used to estimate the confirmation delays of the transactions given the offered fees.
On the Initial Behavior Monitoring Issues in Federated Learning

In Federated Learning (FL), a group of workers participate to build a global model under the coordination of one node, the chief.Regarding the cybersecurity of FL, some attacks aim at injecting the fabricated local model updates into the system.Some defenses are based on malicious worker detection and behavioral pattern analysis.In this context, without timely and dynamic monitoring methods, the chief cannot detect and remove the malicious or unreliable workers from the system.Our work emphasize the urgency to prepare the federated learning process for monitoring and eventually behavioral pattern analysis.We study the information inside the learning process in the early stages of training, propose a monitoring process and evaluate the monitoring period required.The aim is to analyse at what time is it appropriate to start the detection algorithm in order to remove the malicious or unreliable workers from the system and optimise the defense mechanism deployment.We tested our strategy on a behavioral pattern analysis defense applied to the FL process of different benchmark systems for text and image classification.Our results show that the monitoring process lowers false positives and false negatives and consequently increases system efficiency by enabling the distributed learning system to achieve better performance in the early stage of training.
Single node deep learning frameworks: Comparative study and CPU/GPU performance analysis

Abstract Deep learning presents an efficient set of methods that allow learning from massive volumes of data using complex deep neural networks. To facilitate the design and implementation of algorithms, deep learning frameworks provide a high‐level programming interface. Based on these frameworks, new models, and applications are able to make better and better predictions. One type of deep learning application is the Internet of Things that can gather a continuous flow of data, which causes an explosion of the amount of data. Therefore, to handle this data management issue, computation technologies can offer new perspectives to analyze more data with more complex models. In this context, a cluster of computers can operate to quickly deliver a model or to enable the design of a complex neural network spread among computers. An alternative is to distribute a deep learning task with HPC cloud computing resources and to scale cluster in order to quickly and efficiently train a neural network. As a first step to design an infrastructure aware framework which is able to scale the computing nodes, this work aims to review and analyze the state‐of‐the‐art frameworks by collecting device utilization data during the training task. We gather information about the CPU, RAM and the GPU utilization on deep learning algorithms with and without multi‐threading. The behavior of each framework is discussed and analyzed in order to shed light on the strengths and weaknesses of the different deep learning frameworks.
<i>Anthropology and blockchain</i>

Blockchain and its related technologies break away from the contemporary dystopian imaginaries of control and exploitation endemic in IT. This editorial considers the relevance of blockchain for anthropologists, why they should care, and what the technology brings. After sketching the evolution of blockchain, we draw attention to its potential as a playground – a plethora of projects reimagining and remaking the basic stuff of political economy, including the meaning of money, collectivities, exchange and voting. Blockchain's utility for rethinking the basic rules of the game in academia also deserves attention.
FeLU:A Fractional Exponential Linear Unit

Appropriate activation functions need to be selected to train the complex neural networks. Considering the dead zone problem of ReLU function. Therefore, an Exponential Linear Unit (eLU) is proposed for improvement. In this paper, by observing the function curve characteristics of different activation function, network training comparison analysis for different functions, on the basis of eLU activation function adds some is advantageous to the features of network performance, such as non monotonicity, limit tends to zero, and keep the landed weight etc., thus put forward a kind of fractional exponential linear unit (FeLU). Experiments show that FeLU function is superior to eLU function in different depth models and training data sets, and it is an activation function that can effectively improve network performance.
Validity of a Content Agnostic Game Based Stealth Assessment

In an attempt to predict the learning of a player during a content agnostic educational video game session, this study used a dynamic bayesian network in which participants’ game play interactions were continuously recorded. Their actions were captured and used to make real-time inferences of the learning performance using a dynamic bayesian network. The predicted learning was then correlated with the post-test scores to establish the validity of assessment. The assessment was moderately positively correlated with the post-test scores demonstrating support for its validity.
Design and Implementation of Mobile Phone Intercom App Based on Android

In order to meet the needs of instant messaging in many fields of work, this article designs an instant messaging application based on Android smartphones. The application is based on the C/S software architecture model and uses the Netty network framework to communicate between the client and the server. The system mainly realizes that the user enters the user data in the login interface, and then enters the intercom interface after the system verifies the information. The user can select the channel to enter the designated channel and talk in the channel without interference between the channels., The user can choose the channel arbitrarily, and can exit after the intercom. And users can invite other online users to enter a temporary channel for intercom communication. System: The client uses Android Studio, the server uses Eclipse, and the MYSQL database is used to store system data. The voice input of the client uses the AudioRecord class and AudioTrack class for real-time voice communication. The client-server communication in the entire system is implemented based on the Netty network framework[1]. The use of this framework makes the entire communication process simple. The system has been developed. After logging in to the application, the user can select a channel for intercom between multiple users in the local area network. The user can use the application to save professional intercom equipment and communicate with each other using a mobile phon.
Boosting the Speed of Real-Time Multi-Object Trackers

Object tracking is an important computer vision task that has drawn increasing attention because of its academic and commercial potential. Although several trackers based on convolutional neural network (CNN) have shown strong performance in single object tracking and multiple object tracking, it remains challenging to perform real-time object tracking on videos with frame rate over 30 frame per second. For real-world applications, one of the necessary requirements is the trackers need to estimate the bounding boxes with a rate higher or equal to the video frame rate to avoid latency. In this study, we propose a hybrid tracker combining the high accuracy of CNN-based trackers and the superior processing speed of particle filtering algorithm to achieve real-time tracking. The experimental results indicate our tracker achieves a state-of-the-art performance on the benchmark multi-object tracking datasets with competitive accuracy.
Human-centric Requirements Engineering for Artificial Intelligence Software Systems

The surge in data availability and processing power has made it possible for Artificial Intelligence (AI) to advance at a faster rate. However, the different nature of AI systems has posed significant new challenges to Requirements Engineering (RE). Literature has shown that AI systems do not use current RE methods. It was also found that data scientists are taking the role of the requirements engineers resulting in software that does not focus on users needs. Building AI software with a human-centric approach has proven to produce more ethical, transparent, inclusive and non-bias outcomes. This research will look into adjusting current RE methodologies to fit into AI systems from a human-centric perspective. The project will aim to establish requirements specifications for human-centric AI and map them into a modeling language. A platform will be used to visually model and present requirements. Finally, I plan to conduct a case study to evaluate the modeling language. To date, I have conducted a Systematic Literature Review (SLR) to find current RE methodologies and challenges in AI and currently in the planning phase of a survey to find adopted practices in the industry.
Immersive Analytics: A User-Centered Perspective

Researchers have explored using VR and 3D data visualizations for analyzing and presenting data for several decades. Surveys of the literature in the field usually adopt a technical or systemic lens. We propose a survey of the Immersive Analytics literature from the user's perspective that relates the purpose of the visualization to its technical qualities. We present our preliminary review to describe how device technologies, kinds of representation, collaborative features, and research design have been utilized to accomplish the purpose of the visualization. This poster demonstrates our preliminary investigation, inviting feedback from the VRST community. Our hope is the final version of our review will benefit designers, developers, and practitioners who want to implement immersive visualizations from a Human-Centered Design perspective, and help Immersive Analytics researchers get a better understanding of the gaps in current literature.
Evaluating Forecasting, Knowledge, and Visual Analytics

In this paper, we explore the intersection of knowledge and the forecasting accuracy of humans when supported by visual analytics. We have recruited 40 experts in machine learning and trained them in the use of a box office forecasting visual analytics system. Our goal was to explore the impact of visual analytics and knowledge in human-machine forecasting. This paper reports on how participants explore and reason with data and develop a forecast when provided with a predictive model of middling performance (R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ≈ .7). We vary the knowledge base of the participants through training, compare the forecasts to the baseline model, and discuss performance in the context of previous work on algorithmic aversion and trust.
Causal Discovery with Confounding Cascade Nonlinear Additive Noise Models

Identification of causal direction between a causal-effect pair from observed data has recently attracted much attention. Various methods based on functional causal models have been proposed to solve this problem, by assuming the causal process satisfies some (structural) constraints and showing that the reverse direction violates such constraints. The nonlinear additive noise model has been demonstrated to be effective for this purpose, but the model class does not allow any confounding or intermediate variables between a cause pair–even if each direct causal relation follows this model. However, omitting the latent causal variables is frequently encountered in practice. After the omission, the model does not necessarily follow the model constraints. As a consequence, the nonlinear additive noise model may fail to correctly discover causal direction. In this work, we propose a confounding cascade nonlinear additive noise model to represent such causal influences–each direct causal relation follows the nonlinear additive noise model but we observe only the initial cause and final effect. We further propose a method to estimate the model, including the unmeasured confounding and intermediate variables, from data under the variational auto-encoder framework. Our theoretical results show that with our model, the causal direction is identifiable under suitable technical conditions on the data generation process. Simulation results illustrate the power of the proposed method in identifying indirect causal relations across various settings, and experimental results on real data suggest that the proposed model and method greatly extend the applicability of causal discovery based on functional causal models in nonlinear cases.
Parallel learner: A practical deep reinforcement learning framework for multi-scenario games

Traditional reinforcement learning methods are only applicable to single-scenario tasks. When it comes to multi-scenario, the single-scenario agents fail to perform well. That is, the traditional reinforcement learning methods own the poor generalization when facing different tasks simultaneously. In this work, we propose a practical deep reinforcement learning framework that can perform on multiple 3D scenarios concurrently. We adopt the Actor–Learner framework to realize the parallelization of multiple scenarios and resolve the policy lag problem by generalizing Retrace(λ) to a new value function. We prove its convergence theoretically. Besides, we design an auxiliary recognition task and an auxiliary control task inspired by the hard shared representation in multi-task learning to improve the performance of our multi-scenario agent. Experimental results show that our method outperforms state-of-the-art algorithms on DMLab-30, achieving more advantages on multi-scenario games. We verify the effectiveness of each part of our framework by the ablation experiments. We also find our parallel learner transferable by testing on the untrained scenarios.
AR Learning Environment Integrated with EIA Inquiry Model: Enhancing Scientific Literacy and Reducing Cognitive Load of Students

This study constructed the EIA (Experience–Inquiry–Application) model to evaluate its extent on promoting scientific inquiry activities under an AR learning environment in an upper primary science course setting. Two hundred and nine fifth-grade Chinese students were randomly assigned to one of the three conditions, as a quasi-experiment was conducted to investigate how the EIA model and the AR learning environment influence students’ science learning. Both quantitative and qualitative data were collected. Quantitative data suggest that students who participated in the EIA model under the AR setting performed the best; it also gives evidence to support that both the EIA model and the AR environment has significant positive effects on students’ performance in science learning. Qualitative data, in the form of a semi-structured interview with teachers and students, reveal that AR is able to be used for experiments that were originally deemed impossible, and it inspires students’ motivation for knowledge acquisition. Moreover, the EIA model empowers students in small-group collaboration, and is a good pedagogical tool to summarize units. EIA and AR form a bond of theory and technology and it strengthens students in manifold ways when it is deeply interwoven.