Reviewerly/exHarmony huggingface dataset:
Specialization in Hierarchical Learning Systems

Abstract Joining multiple decision-makers together is a powerful way to obtain more sophisticated decision-making systems, but requires to address the questions of division of labor and specialization. We investigate in how far information constraints in hierarchies of experts not only provide a principled method for regularization but also to enforce specialization. In particular, we devise an information-theoretically motivated on-line learning rule that allows partitioning of the problem space into multiple sub-problems that can be solved by the individual experts. We demonstrate two different ways to apply our method: (i) partitioning problems based on individual data samples and (ii) based on sets of data samples representing tasks. Approach (i) equips the system with the ability to solve complex decision-making problems by finding an optimal combination of local expert decision-makers. Approach (ii) leads to decision-makers specialized in solving families of tasks, which equips the system with the ability to solve meta-learning problems. We show the broad applicability of our approach on a range of problems including classification, regression, density estimation, and reinforcement learning problems, both in the standard machine learning setup and in a meta-learning setting.
Learning Spatio-temporal features to detect manipulated facial videos created by the Deepfake techniques

In the last years, the face synthetic video generation has been rapid, and hyper-realistic forged videos are based on Deepfake techniques. It leads to a loss of trust in videos' content and makes it malicious by spreading forged videos on the internet. Until now, there are a few algorithms that have been suggested for detecting forged videos created by Deepfake techniques, but most of them based on analyzing or learning features on frames separately in a video. Those algorithms often pay less attention to Spatio-temporal features, so these algorithms' accuracy is usually not good. This paper proposes a 3-dimensional (3-D) convolutional neural network model that can learn Spatio-temporal features from an adjacent frame sequence in a video. Our proposed network's binary detection accuracy reached over 99% on the two largest benchmark datasets as Deepfake of FaceForensics++ and VidTIMIT datasets. The experimental results of the proposed method outperform state-of-the-art methods.
MIND-Net: A Deep Mutual Information Distillation Network for Realistic Low-Resolution Face Recognition

Realistic low-resolution (LR) face images refer to those captured by the real-world surveillance cameras at extreme standoff distances, thereby LR and poor in quality essentially. Owing to severe scarcity of labeled data, a high-capacity deep convolution neural networks (CNN) is hardly trained to confront the realistic LR face recognition (LRFR) challenge. We introduce in this letter a dual-stream mutual information distillation network (MIND-Net), whereby the non-identity specific mutual information (MI) characterized by generic face features coexistent on realistic and synthetic LR face images are distilled to render a resolution-invariant embedding space for LRFR. For a thorough analysis, we quantify the degree of MI distillation in terms normalized MI index. Our experimental results on the realistic LR face datasets substantiate that the MIND-Net instances assembled from the pre-learned CNNs stand out from the baselines and other state of the arts by a notable margin.
Interaction-Integrated Network for Natural Language Moment Localization

Natural language moment localization aims at localizing video clips according to a natural language description. The key to this challenging task lies in modeling the relationship between verbal descriptions and visual contents. Existing approaches often sample a number of clips from the video, and individually determine how each of them is related to the query sentence. However, this strategy can fail dramatically, in particular when the query sentence refers to some visual elements that appear outside of, or even are distant from, the target clip. In this paper, we address this issue by designing an Interaction-Integrated Network (I <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> N), which contains a few Interaction-Integrated Cells (I <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> Cs). The idea lies in the observation that the query sentence not only provides a description to the video clip, but also contains semantic cues on the structure of the entire video. Based on this, I <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> Cs go one step beyond modeling short-term contexts in the time domain by encoding long-term video content into every frame feature. By stacking a few I <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> Cs, the obtained network, I <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> N, enjoys an improved ability of inference, brought by both (I) multi-level correspondence between vision and language and (II) more accurate cross-modal alignment. When evaluated on a challenging video moment localization dataset named DiDeMo, I <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> N outperforms the state-of-the-art approach by a clear margin of 1.98%. On other two challenging datasets, Charades-STA and TACoS, I <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> N also reports competitive performance.
What’s holding back blockchain finance? On the possibility of decentralized autonomous finance

Despite the past decade’s rapid innovation in adapting blockchain technology to new uses, financial intermediation remains elusive except in basic and highly collateralized forms. We introduce the concept of the technical frontier to delimit the kinds of interactions that can feasibly be structured algorithmically among pseudonymous agents, as on a blockchain, and show that lending and financial intermediation – unlike monetary exchange – lie outside it, even in simple forms. The path forward for truly blockchain-native financial applications, therefore, must involve the integration of real-world identity information in order to disincentivize defection. We discuss several potential technologies for doing so, and conclude that such integration is possible without compromising pseudonymity, provided real-world identity is available in the breach.
Progressive Modality Cooperation for Multi-Modality Domain Adaptation

In this work, we propose a new generic multi-modality domain adaptation framework called Progressive Modality Cooperation (PMC) to transfer the knowledge learned from the source domain to the target domain by exploiting multiple modality clues (e.g., RGB and depth) under the multi-modality domain adaptation (MMDA) and the more general multi-modality domain adaptation using privileged information (MMDA-PI) settings. Under the MMDA setting, the samples in both domains have all the modalities. Through effective collaboration among multiple modalities, the two newly proposed modules in our PMC can select the reliable pseudo-labeled target samples, which captures the modality-specific information and modality-integrated information, respectively. Under the MMDA-PI setting, some modalities are missing in the target domain. Hence, to better exploit the multi-modality data in the source domain, we further propose the PMC with privileged information (PMC-PI) method by proposing a new multi-modality data generation (MMG) network. MMG generates the missing modalities in the target domain based on the source domain data by considering both domain distribution mismatch and semantics preservation, which are respectively achieved by using adversarial learning and conditioning on weighted pseudo semantic class labels. Extensive experiments on three image datasets and eight video datasets for various multi-modality cross-domain visual recognition tasks under both MMDA and MMDA-PI settings clearly demonstrate the effectiveness of our proposed PMC framework.
REA, Triple-Entry Accounting and Blockchain: Converging Paths to Shared Ledger Systems

The concept of shared ledger systems offering a single source of truth has repeatedly called traditional bookkeeping into question. Improving upon the long-standing double-entry system, solutions such as the Resource-Event-Agent (REA) accounting framework, triple-entry accounting (TEA) and blockchain have been advanced. However, to date, the historical development of these concepts remains murky. This paper conducts a genealogical analysis of shared ledger systems, in particular tracing the development of REA, TEA and blockchain. We show how the REA framework has had a distinct influence on independent streams of research in the field of TEA, and how this interaction may be traced to the present incarnation of shared ledger systems in blockchain. In doing so, we duly acknowledge the influence of key individuals contributing to this development, correct common misconceptions and map out how the paths of REA, TEA and blockchain overlap in the realm of shared ledger systems.
Motion Planning of Mobile Robots for Autonomous Navigation on Uneven Ground Surfaces

With the increasing interest in mobile robots for construction applications, autonomous navigation in unstructured and uneven construction sites has become a critical challenge. To ensure the safe and robust navigation of a robot in such environments, this study develops an optimal obstacle-avoiding path planner for stable posture (OOPS) that minimizes the distance to the goal area and stabilizes the posture of the robot. In this study, a new algorithm was developed by significantly improving the quick-rapidly exploring random tree* (Q-RRT*) algorithm to find a feasible path and converge to the optimal path more quickly. A cost function was defined to stabilize the posture of the robot. A simulation experiment was conducted to examine the feasibility of the OOPS algorithm, and its performance was compared with those of other algorithms. The OOPS algorithm was also validated in an experiment in a real-world outdoor environment with sloped hills and several obstacles. The results demonstrate that the OOPS algorithm outperforms other algorithms in terms of the time needed to find the initial solution, time to convergence on the optimal solution, and rate of success in reaching the goal. A mobile robot with the OOPS algorithm is able to start navigating sooner, and the algorithm takes less time than other algorithms to produce paths that are closer to the optimal path. The robot's posture is more stable when it follows the path obtained from the OOPS algorithm in both simulation and real-world tests. Therefore, the OOPS algorithm can be effectively used for applications in uneven, highly sloped, and unstructured outdoor environments.
Attitudes towards Social Robots in Education: Enthusiast, Practical, Troubled, Sceptic, and Mindfully Positive

While social robots bring new opportunities for education, they also come with moral challenges. Therefore, there is a need for moral guidelines for the responsible implementation of these robots. When developing such guidelines, it is important to include different stakeholder perspectives. Existing (qualitative) studies regarding these perspectives however mainly focus on single stakeholders. In this exploratory study, we examine and compare the attitudes of multiple stakeholders on the use of social robots in primary education, using a novel questionnaire that covers various aspects of moral issues mentioned in earlier studies. Furthermore, we also group the stakeholders based on similarities in attitudes and examine which socio-demographic characteristics influence these attitude types. Based on the results, we identify five distinct attitude profiles and show that the probability of belonging to a specific profile is affected by such characteristics as stakeholder type, age, education and income. Our results also indicate that social robots have the potential to be implemented in education in a morally responsible way that takes into account the attitudes of various stakeholders, although there are multiple moral issues that need to be addressed first. Finally, we present seven (practical) implications for a responsible application of social robots in education following from our results. These implications provide valuable insights into how social robots should be implemented.
Saliency Prediction on Omnidirectional Image With Generative Adversarial Imitation Learning

When watching omnidirectional images (ODIs), subjects can access different viewports by moving their heads. Therefore, it is necessary to predict subjects' head fixations on ODIs. Inspired by generative adversarial imitation learning (GAIL), this paper proposes a novel approach to predict saliency of head fixations on ODIs, named SalGAIL. First, we establish a dataset for attention on ODIs (AOI). In contrast to traditional datasets, our AOI dataset is large-scale, which contains the head fixations of 30 subjects viewing 600 ODIs. Next, we mine our AOI dataset and discover three findings: (1) the consistency of head fixations are consistent among subjects, and it grows alongside the increased subject number; (2) the head fixations exist with a front center bias (FCB); and (3) the magnitude of head movement is similar across the subjects. According to these findings, our SalGAIL approach applies deep reinforcement learning (DRL) to predict the head fixations of one subject, in which GAIL learns the reward of DRL, rather than the traditional human-designed reward. Then, multi-stream DRL is developed to yield the head fixations of different subjects, and the saliency map of an ODI is generated via convoluting predicted head fixations. Finally, experiments validate the effectiveness of our approach in predicting saliency maps of ODIs, significantly better than 11 state-of-the-art approaches. Our AOI dataset and code of SalGAIL are available online at https://github.com/yanglixiaoshen/SalGAIL.
Governance and Domestic Investment in Africa

The study empirically examined the impact of governance on domestic investment in 16 African countries with a balanced panel data set, between the years 2002 and 2015. The study employed six unbundled governance indicators from the World Bank, World Governance Indicators and constructed three bundled governance indicators using the Principal Component Analysis. The Driscoll and Kraay Fixed Effects model which accounts for serial correlation, groupwise heteroskedasticity and cross-sectional dependence were employed with empirical results revealing that all the indicators of governance positively and significantly influence domestic investment in Africa, except for government effectiveness which happens to be insignificant. Also, Voice/Accountability and the Control of Corruption exert more influence on domestic investment as indicated by their coefficient values. Furthermore, economic growth is also an important factor in explaining domestic investment in Africa. Policy recommendations are discussed.
Continuous leakage-resilient certificate-based signcryption scheme and application in cloud computing

Leakage of private information, e.g. the secret keys, has become a serious threat to the security of computing systems. It has become a common requirement that real-world security applications should withstand various leakage attacks, such as side-channel attacks, cold-boot attacks, etc. For example, the above leakage attacks are very common in cloud computing nowadays. Hence, we need a novel method to protect the security of data storage and authorization even if a certain amount of leakage information with respect to the secret state can be obtained by any adversary. In order to achieve the above goal, in this paper, we introduce a continuous leakage-resilient certificate-based signcryption (CBS) scheme, and we prove that our proposed scheme achieves the chosen-ciphertext attacks (CCA) security based on the discrete logarithm assumption and the decisional Diffie-Hellman assumption. Our proposed scheme not only has the ability to resist the continuous leakage attacks, but also enjoys very low computational overheads. Moreover, two concrete continuous leakage-resilient data storage and authorization protocols are generated from the above continuous leakage-resilient CBS scheme: one has a single key generation center and the other generates the keys in a distributed form. Therefore, our protocols with continuous leakage resilience are particularly suitable for data storage and authorization in cloud computing system.
Optimal Load Allocation for Coded Distributed Computation in Heterogeneous Clusters

Recently, coding has been a useful technique to mitigate stragglers' effect in distributed computing. However, coding in this context has been mainly explored assuming homogeneous workers, although real-world clusters often consist of heterogeneous workers with different computing capabilities. The uniform load allocation without considering the heterogeneity possibly causes a significant loss in latency. In this article, we suggest the optimal load allocation for coded distributed computing with heterogeneous workers. Specifically, we focus on the scenario that there exist workers having the same computing capability, which can be regarded as a group for analysis. We rely on the lower bound on the expected latency and obtain the optimal load allocation by showing that our load allocation achieves the minimum of the lower bound for a sufficiently large number of workers. Given the proposed optimal load allocation, we derive the optimal code rate to achieve the minimum expected latency. From numerical simulations, when assuming the group heterogeneity, our load allocation reduces the expected latency by orders of magnitude over the existing scheme. Furthermore, from experiments on Amazon EC2 for scenarios with distinct straggler/heterogeneity patterns, we observe that our scheme outperforms the competing schemes reducing the total finishing time by up to 52%.
Streamer action recognition in live video with spatial-temporal attention and deep dictionary learning

Live video hosted by streamer is being sought after by more and more Internet users. A few streamers show inappropriate action in normal live video content for profit and popularity, who bring great harm to the network environment. In order to effectively regulate the streamer behavior in live video, a streamer action recognition method in live video with spatial-temporal attention and deep dictionary learning is proposed in this paper. First, deep features with spatial context are extracted by a spatial attention network to focus on action region of streamer after sampling video frames from live video. Then, deep features of video are fused by assigning weights with a temporal attention network to learn the frame attention from an action. Finally, deep dictionary learning is used to sparsely represent the deep features to further recognize streamer actions. Four experiments are conducted on a real-world dataset, and the competitive results demonstrate that our method can improve the accuracy and speed of streamer action recognition in live video.
Bitcoin Price Forecasting via Ensemble-based LSTM Deep Learning Networks

Time series prediction plays a significant role in the Bitcoin market because of volatile characteristics. Recently, deep neural networks with advanced techniques such as ensembles have led to studies that show successful performance in various fields. In this paper, an ensemble-enabled Long Short-Term Memory (LSTM) with various time interval models is proposed for predicting Bitcoin price. Although hour and minute data set are shown to provide moderate shifts, daily data has relatively a deterministic shift. As such, the ensemble-enabled LSTM network architecture learned the individual characteristics and impact on price predictions from each data set. Experimental results with real-world measurement data show that this learning architecture effectively forecasts prices, especially in risky time such as sudden price fall.
An improved atom search optimization with dynamic opposite learning and heterogeneous comprehensive learning

Atom search optimization (ASO) is a newly developed metaheuristic algorithm inspired by molecular basis dynamics. The paramount challenge in ASO is that it is prone to stagnation in local optima due to premature convergence. To solve this issue, a modified atom search optimization with dynamic opposite learning and heterogeneous comprehensive learning is proposed in this paper, which is named DOLHCLASO. First, the asymmetry of dynamic opposite learning can increase the probability that the population will obtain an optimal solution, while dynamic characteristic can enrich population diversity and enhance exploration capability. Second, the heterogeneous comprehensive learning divides the population into two subpopulations, exploration-subpopulation and exploitation-subpopulation, and the division of labor and cooperation effectively balances exploration and exploitation. Finally, the proposed DOLHCLASO was evaluated in the CEC2017 benchmark functions and real-world engineering cases, compared with some classic and excellent variants of algorithms to confirm its performance. Experimental results and statistical analysis demonstrate that the performance of the DOLHCLASO is significantly better than other selected optimizers.
LARNet: Real-Time Detection of Facial Micro Expression Using Lossless Attention Residual Network

Facial micro expressions are brief, spontaneous, and crucial emotions deep inside the mind, reflecting the actual thoughts for that moment. Humans can cover their emotions on a large scale, but their actual intentions and emotions can be extracted at a micro-level. Micro expressions are organic when compared with macro expressions, posing a challenge to both humans, as well as machines, to identify. In recent years, detection of facial expressions are widely used in commercial complexes, hotels, restaurants, psychology, security, offices, and education institutes. The aim and motivation of this paper are to provide an end-to-end architecture that accurately detects the actual expressions at the micro-scale features. However, the main research is to provide an analysis of the specific parts that are crucial for detecting the micro expressions from a face. Many states of the art approaches have been trained on the micro facial expressions and compared with our proposed Lossless Attention Residual Network (LARNet) approach. However, the main research on this is to provide analysis on the specific parts that are crucial for detecting the micro expressions from a face. Many CNN-based approaches extracts the features at local level which digs much deeper into the face pixels. However, the spatial and temporal information extracted from the face is encoded in LARNet for a feature fusion extraction on specific crucial locations, such as nose, cheeks, mouth, and eyes regions. LARNet outperforms the state-of-the-art methods with a slight margin by accurately detecting facial micro expressions in real-time. Lastly, the proposed LARNet becomes accurate and better by training with more annotated data.