Reviewerly/exHarmony huggingface dataset:
Text Compression-aided Transformer Encoding

Text encoding is one of the most important steps in Natural Language Processing (NLP). It has been done well by the self-attention mechanism in the current state-of-the-art Transformer encoder, which has brought about significant improvements in the performance of many NLP tasks. Though the Transformer encoder may effectively capture general information in its resulting representations, the backbone information, meaning the gist of the input text, is not specifically focused on. In this paper, we propose explicit and implicit text compression approaches to enhance the Transformer encoding and evaluate models using this approach on several typical downstream tasks that rely on the encoding heavily. Our explicit text compression approaches use dedicated models to compress text, while our implicit text compression approach simply adds an additional module to the main model to handle text compression. We propose three ways of integration, namely backbone source-side fusion, target-side fusion, and both-side fusion, to integrate the backbone information into Transformer-based models for various downstream tasks. Our evaluation on benchmark datasets shows that the proposed explicit and implicit text compression approaches improve results in comparison to strong baselines. We therefore conclude, when comparing the encodings to the baseline models, text compression helps the encoders to learn better language representations.
Simultaneous Long-Distance Transmission of Discrete-Variable Quantum Key Distribution and Classical Optical Communication

We theoretically study the issues for long-distance transmission of quantum key distribution (QKD) coexisting with classical signals. The recently proposed phase-matching QKD protocol can drastically improve the transmission distance of QKD. However, in the coexistence system, the noise generated by classical signals, especially spontaneous Raman scattering noise, is a big challenge. Moreover, the classical optical amplifier, which is necessary for the realistic long-distance classical communication, makes the noise more serious. In view of this, we establish the unified Raman noise model for three discrete-variable QKD protocols (BB84, measurement-device-independent and phase-matching QKD protocols) in the presence of classical optical amplifiers, which can be applied to both single-core single-mode fiber and multicore fiber. Then, we derive the key rate of the three QKD protocols coexisting with classical signals using the proposed unified Raman noise model. Finally, simulation results show that multicore fiber is promising for simultaneous long-distance transmission of QKD and the dense wavelength division multiplexing system.
Verification of phased Dicke states

Dicke states are typical examples of quantum states with genuine multipartite entanglement. They are valuable resources in many quantum information processing tasks, including multiparty quantum communication and quantum metrology. Phased Dicke states are a generalization of Dicke states and include antisymmetric basis states as a special example. These states are useful in atomic and molecular physics besides quantum information processing. Here we propose practical and efficient protocols based on adaptive local projective measurements for verifying all phased Dicke states, including $W$ states and qudit Dicke states. To verify any $n$-partite phased Dicke state within infidelity $\epsilon$ and significance level $\delta$, the number of tests required is only $O(n\epsilon^{-1}\ln\delta^{-1})$, which is linear in $n$ and is exponentially more efficient than traditional tomographic approaches. In the case of $W$ states, the number of tests can be further reduced to $O(\sqrt{n}\,\epsilon^{-1}\ln\delta^{-1})$. Moreover, we construct an optimal protocol for any antisymmetric basis state; the number of tests required decreases (rather than increases) monotonically with $n$. This is the only optimal protocol known for multipartite nonstabilizer states.
Many-body calculations for periodic materials via restricted Boltzmann machine-based VQE

Abstract A state of the art method based on quantum variational algorithms can be a powerful approach for solving quantum many-body problems. However, the research scope in the field is mainly limited to organic molecules and simple lattice models. Here, we propose a workflow of a quantum variational algorithm for periodic systems on the basis of an effective model construction from first principles. The band structures of the Hubbard model of graphene with the mean-field approximation are calculated as a benchmark, and the calculated eigenvalues obtained by restricted Boltzmann machine-based variational quantum eigensolver (RBM-based VQE) show good agreement with the exact diagonalization results within a few meV. The results show that the present computational scheme has the potential to solve many-body problems quickly and correctly for periodic systems using a quantum computer.
Smart Campus Model: A Literature Review

Nowadays a lot of literature explaining the framework of smart campus that includes terminology, definition, framework, and best practice that has been implemented in universities, therefore there needs to be a review of the paper so that it can be taken framework suitable for implementation in Indonesia. This study provides a reference for researchers in the smart campus field also for university stakeholders to gain a better understanding of smart campus terminology and framework. The paper is based on review literature from an online scientific database. This paper will contribute to the smart campus research topic with a focus on smart campus terminology and framework. This paper aims to review Smart Definition, Smart Campus Definition, and Smart Campus model.
Facilitating EFL learners’ active behaviors in speaking: a progressive question prompt-based peer-tutoring approach with VR contexts

Speaking is a challenge for most English learners owing to the lack of practicing environments and partners. The virtual reality (VR) technology has changed this situation by providing learners with rich opportunities for practice. In VR-based practicing environments, peer tutoring is a common strategy for promoting peer assistance. However, in conventional VR-based learning, students may merely follow the guidelines and content provided by the teacher without thinking about the meaning and reasons for the practice in the situated contexts, which generally leads to poor learning outcomes. To solve this problem, a progressive question prompt-based peer-tutoring approach in VR contexts (PQP-PTVR) is proposed to assist students’ English-speaking development. An experiment was conducted to evaluate the effectiveness of the proposed approach. The experimental group used the proposed approach, while the control group learned with the conventional question prompt-based peer-tutoring approach in VR contexts (C-PTVR). The results showed that the experimental group achieved significantly greater improvement in speaking and self-efficacy than the control group. Concerning the participants’ learning behaviors, the experimental group had more interactions and made more attempts to improve their speaking. This study contributes to the existing literature by proposing the PQP-PTVR approach to enhancing learners’ speaking development, improving their interactions and self-efficacy.
Supporting Caring among Intergenerational Family Members through Family Fitness Tracking

We present results from a qualitative study involving eight intergenerational families (27 participants) to understand how a family tracking intervention can help support care among intergenerational family members. Our findings show that family members communicate and stay aware of each other's' health through shared fitness data and conversations triggered by fitness sharing. We identified different challenges and preferences among the three age groups in our study: older adults enjoyed family fitness sharing but often encountered various technical challenges, the middle-aged group served as a key person to care for the rest of the family members, and the young generation could not fully engage in fitness sharing due to their busy schedule and privacy concerns. These findings suggest the design of family fitness sharing to account for the age differences in intergenerational families and support the unique needs of family fitness sharing.
CURATING: A multi-objective based pruning technique for CNNs

As convolutional neural networks (CNNs) improve in accuracy, their model size and computational overheads have also increased. These overheads make it challenging to deploy the CNNs on resource-constrained devices. Pruning is a promising technique to mitigate these overheads. In this paper, we propose a novel pruning technique called CURATING that looks at the pruning of CNNs as a multi-objective optimization problem. CURATING retains filters that (i) are very different (less redundant) from each other in terms of their representation (ii) have high saliency score i.e., they reduce the model accuracy drastically if pruned (iii) are likely to produce higher activations. We treat a filter specific to an output channel as a probability distribution over spatial filters to measure the similarity between filters. The similarity matrix is leveraged to create filter embeddings, and we constrain our optimization problem to retain a diverse set of filters based on these filter embeddings. On a range of CNNs over well-known datasets, CURATING exercises a better or comparable tradeoff between model size, accuracy, and inference latency than existing techniques. For example, while pruning VGG16 on the ILSVRC-12 dataset, CURATING achieves higher accuracy and a smaller model size than the previous techniques.
Steganography in color images with random order of pixel selection and encrypted text message embedding

Information security is major concern in modern digital ages, and the outdated algorithms need to be replaced with new ones or to be improved. In this article a new approach for hiding secret text message in color images is presented, combining steganography and cryptography. The location and the order of the image pixels chosen for information embedding are randomly selected using chaotic pseudo-random generator. Encrypting the secret message before embedding is another level of security designed to misguide the attackers in case of analyzing for traces of steganography. Evaluating the proposed stegoalgorithm. The standard statistical and empirical tests are used for randomness tests, key-space analysis, key-sensitivity analysis, visual analysis, histogram analysis, peak signal-to-noise ratio analysis, chi-square analysis, etc. The obtained results are presented and explained in the present article.
Parallel/Distributed Generative Adversarial Neural Networks for Data Augmentation of COVID-19 Training Images

This article presents an approach using parallel/distributed generative adversarial networks for image data augmentation, applied to generate COVID-19 training samples for computational intelligence methods. This is a relevant problem nowadays, considering the recent COVID-19 pandemic. Computational intelligence and learning methods are useful tools to assist physicians in the process of diagnosing diseases and acquire valuable medical knowledge. A specific generative adversarial network approach trained using a co-evolutionary algorithm is implemented, including a three-level parallel approach combining distributed memory and fine-grained parallelization using CPU and GPU. The experimental evaluation of the proposed method was performed on the high performance computing infrastructure provided by National Supercomputing Center, Uruguay. The main experimental results indicate that the proposed model is able to generate accurate images and the $$3\times 3$$ version of the distributed GAN has better robustness properties of its training process, allowing to generate better and more diverse images.
A Trusted Consensus Scheme for Collaborative Learning in the Edge AI Computing Domain

Collaborative learning with multiple edge devices to build group intelligence is a new trend. Edge artificial intelligence (AI) computing often makes full use of various available data and resources in terminal devices, edge servers, and cloud data centers to achieve collaborative deci-sion-making. However, in order to achieve the goal, it should also guarantee the security of data storage, transmission, and management. Therefore, how to build a distributed trust system for cooperative learning in edge computing is a complex and challenging engineering problem. Block-chain can help realize collaborative learning in a distributed way without the need for third-party audit. Also, the consensus protocol plays a key technology role. In this article, a trusted consensus scheme for multi-party collaborative learning of edge AI is proposed. To improve system security, reputation-based rights are used to support fast removal of abnormal nodes. With the method of reputation rewards and punishments, the reputations of the nodes participating in the consensus process are scored and recorded on the chain according to their behavior. Nodes who commit malicious actions will be punished by reducing their reputation values. Nodes are rated according to their reputation value, and those with higher reputation rates are acknowledged with more credits and rights in consensus. Since the reputation module is loadable on X-BFT protocols, it is easy to enforce in real applications. The experimental results show that the probability of the attacker being chosen as the leader of XR-BFT is 87.5 percent lower than that of the X-BFT protocols, thus enabling our protocol to achieve trust collaborative learning in edge AI computing with more safety and efficiency.
A Survey of the Dark Web and Dark Market Research

The Dark Web is a web-based content based on anonymous communication systems that protects users and protects anonymity through technologies such as traffic encryption, obfuscation, and forwarding. Due to the strong protection of anonymity, the Dark Web has gradually become the main channel for the sale and distribution of harmful content and banned items. The limited web-site coverage in the Dark Web, outdated stale data, and the lack of comprehensive and in-depth analysis and research motivated us to conduct an in-depth Dark Web research. On this basis, this article analyzes and summarizes the current status of Dark Web communication technology, Dark Web research methods and research status, and discusses the challenges of Dark Web re-search.
F-Siamese Tracker: A Frustum-based Double Siamese Network for 3D Single Object Tracking

This paper presents F-Siamese Tracker, a novel approach for single object tracking prominently characterized by more robustly integrating 2D and 3D information to reduce redundant search space. A main challenge in 3D single object tracking is how to reduce search space for generating appropriate 3D candidates. Instead of solely relying on 3D proposals, firstly, our method leverages the Siamese network applied on RGB images to produce 2D region proposals which are then extruded into 3D viewing frustums. Besides, we perform an on-line accuracy validation on the 3D frustum to generate refined point cloud searching space, which can be embedded directly into the existing 3D tracking backbone. For efficiency, our approach gains better performance with fewer candidates by reducing search space. In addition, benefited from introducing the online accuracy validation, for occasional cases with strong occlusions or very sparse points, our approach can still achieve high precision, even when the 2D Siamese tracker loses the target. This approach allows us to set a new state-of-the-art in 3D single object tracking by a significant margin on a sparse outdoor dataset (KITTI tracking). Moreover, experiments on 2D single object tracking show that our framework boosts 2D tracking performance as well.
Explainable Anomaly Detection for District Heating Based on Shapley Additive Explanations

One key component in the heat-using facility of district heating systems is the differential pressure control valve. This valve ensures a stable flow of water to the heat exchanger and the temperature control valve. It also makes a stable pressure difference between the supply and return lines. Hence, its malfunctioning could cause significant heat losses and, consequently, economic losses. To avoid this, it is necessary to monitor the abnormal operation of the valve in real-time. Despite various machine learning-based anomaly detection models, their decision is limited in practical use unless the rationale for the decision is appropriately explained. In this paper, we propose a Shapley additive explanation-based explainable anomaly detection scheme that can present the degree of contribution of input variables to the derived result. We report some of the experimental results.
Programming cobots by voice: A human-centered, web-based approach

We present a new voice-based programming approach and software framework for collaborative robots (cobots) based on the Web Speech API, which is now supported by most modern browsers. The framework targets human programmable interfaces (HPIs), which can be used by people with little or no programming experience. The framework follows a meta-programming approach by enabling users to program cobots by voice in addition to using a mouse, tablet or keyboard. Upon a voice instruction (such as move, pick, release, etc.), the framework automates the manual tasks required to manipulate the vendor-provided HPI. The main advantages of this approach are simplified, guided programming, which only requires the knowledge of 5–10 voice instructions; increased programming speed by up to 46% compared to the manual approach; and the possibility of sharing programs as videos. The open-source framework was evaluated using two application scenarios.
A Secure and Scalable Framework for Blockchain Based Edge Computation Offloading in Social Internet of Vehicles

The integration of Internet of Vehicles (IoV) with social networks has introduced Social IoV (SIoV) that will offer new applications in vehicular networks, e.g., personalized recommendations and route planning. This will be facilitated by heterogeneous access technologies and edge computing to offload tasks from vehicles via secure resource assignment. Thus, each vehicle in SIoV acts as a social subject that manages its own network. SIoV will lead to an explosive growth in network size, and induce issues like scalability and resource discovery. Blockchain is a potential candidate to address these, however, it is not suitable for SIoV with traditional proof-of-work (PoW) consensus. In this paper, we propose a framework that uses a dynamic PoW (dPoW) consensus with a checkpoint mechanism and a resource assignment policy. The dPoW consensus has different mining difficulty levels that change according to the communication traffic, whereas the checkpoint defines an alternative mechanism to generate the next block hash. The assignment policy manages an access control list to mandate the edge modules to securely distribute resources among vehicles. To study the feasibility of our framework, we present a formal security analysis using the Access Control Logic model. For the performance analysis, we use three metrics, i.e., scalability, latency, and security. With these analyses, we demonstrate that our framework offers enhanced security and can scale with a minimal increase in computation overhead. A case study with a comparative analysis is also discussed that evaluates the network dynamics and attests the superior performance of the framework under a real-life vehicular network scenario.
Performance evaluation of Attribute-Based Encryption on constrained IoT devices

The Internet of Things (IoT) is enabling a new generation of innovative services based on the seamless integration of smart objects into information systems. This raises new security and privacy challenges that require novel cryptographic methods. Attribute-Based Encryption (ABE) is a type of public-key encryption that enforces a fine-grained access control on encrypted data based on flexible access policies. The feasibility of ABE adoption in fully-fledged computing systems, i.e., smartphones or embedded systems, has been demonstrated in recent works. In this paper, we consider IoT devices characterized by strong limitations in terms of computing, storage, and power. Specifically, we assess the performance of ABE in typical IoT constrained devices. We evaluate the performance of three representative ABE schemes configured considering the worst-case scenario on two popular IoT platforms, namely ESP32 and RE-Mote. Our results show that, if we assume to employ up to 10 attributes in ciphertexts and to leverage hardware cryptographic acceleration, then ABE can indeed be adopted on devices with very limited memory and computing power, while obtaining a satisfactory battery lifetime. In our experiments, as also performed in other works in the literature, we consider only the worst-case configuration, which, however, might not be completely representative of the real working conditions of sensors employing ABE. For this reason, we complete our evaluation by proposing a novel benchmark method that we used to complement the experiments by evaluating the average performance. We show that by always considering the worst case, the current literature significantly overestimates the processing time and the energy consumption.
Me-Momentum: Extracting Hard Confident Examples from Noisily Labeled Data

Examples that are close to the decision boundary—that we term hard examples, are essential to shape accurate classifiers. Extracting confident examples has been widely studied in the community of learning with noisy labels. However, it remains elusive how to extract hard confident examples from the noisy training data. In this paper, we propose a deep learning paradigm to solve this problem, which is built on the memorization effect of deep neural networks that they would first learn simple patterns, i.e., which are defined by these shared by multiple training examples. To extract hard confident examples that contain non-simple patterns and are entangled with the inaccurately labeled examples, we borrow the idea of momentum from physics. Specifically, we alternately update the confident examples and refine the classifier. Note that the extracted confident examples in the previous round can be exploited to learn a better classifier and that the better classifier will help identify better (and hard) confident examples. We call the approach the "Momentum of Memorization" (Me-Momentum). Empirical results on benchmark-simulated and real-world label-noise data illustrate the effectiveness of Me-Momentum for extracting hard confident examples, leading to better classification performance.