Reviewerly/exHarmony huggingface dataset:
Statistical Statements in Probabilistic Logic Programming

Abstract Probabilistic Logic Programs under the distribution semantics (PLPDS) do not allow statistical probabilistic statements of the form “90% of birds fly”, which were defined “Type 1” statements by Halpern. In this paper, we add this kind of statements to PLPDS and introduce the PASTA (“Probabilistic Answer set programming for STAtistical probabilities”) language. We translate programs in our new formalism into probabilistic answer set programs under the credal semantics. This approach differs from previous proposals, such as the one based on “probabilistic conditionals” as, instead of choosing a single model by making the maximum entropy assumption, we take into consideration all models and we assign probability intervals to queries. In this way we refrain from making assumptions and we obtain a more neutral framework. We also propose an inference algorithm and compare it with an existing solver for probabilistic answer set programs on a number of programs of increasing size, showing that our solution is faster and can deal with larger instances.
Masked Face Recognition From Synthesis to Reality

As we have been seriously hit by the COVID-19 pandemic, wearing a facial mask is a crucial action that we can take for our protection. This paper reports a comprehensive study on the recognition of masked faces. By using facial landmarks, we synthesize the facial mask for each face in several benchmark databases with different challenging factors. The IJB-B and IJB-C databases are selected for evaluating the performance against the variation across pose, illumination and expression (PIE). The FG-Net database is selected for evaluating the performance across age. The SCface is chosen for evaluating the performance on low-resolution images. The MS-1MV2 is exploited as the base training set. We use the ResNet-100 as the feature embedding network connected to state-of-the-art loss functions designed for tackling face recognition. The loss functions considered include the Center Loss, the Marginal Loss, the Angular Softmax Loss, the Large Margin Cosine Loss and the Additive Angular Margin Loss. Both verification and identification are conducted in our evaluation. The performances for recognizing faces with and without the synthetic masks are all evaluated for a complete comparison. The network with the best loss function for recognizing synthetic masked faces is then assessed on a real masked face database, the cleaned RMFRD (c-RMFRD) dataset. Compared with a human user test on the c-RMFRD, the network trained on the synthetic masked faces outperforms human vision for a large gap. Our contributions are fourfold. The first is a comprehensive study for tackling masked face recognition by using state-of-the-art loss functions against various compounding factors. For comparison purpose, the second is another comprehensive study on the recognition of faces without masks by using the same loss functions against the same challenging factors. The third is the verification of the network trained on synthetic masked faces for tackling the real masked face recognition with performance better than human inspectors. The fourth is the highlight on the challenges of masked face recognition and the directions for future research. Our code, trained models and dataset are available via the project GitHub site.
Federated Learning via Over-the-Air Computation With Statistical Channel State Information

Federated learning (FL) is a popular distributed learning paradigm, in which a global model at a server learns private data of clients without data shared among clients or the server. In this paper, we consider FL over a noisy fading multiple access channel (MAC) via over-the-air computation (AirComp). Benefiting from waveform-superposition propriety of wireless signals, AirComp is able to achieve fast aggregations in FL and improve spectral efficiency. However, most of the schemes exploiting AirComp require intensive channel estimations as the demands from precoders, which results in considerable communication overheads. For this reason, we propose two novel FL schemes with statistical channel state information (FL-SCSI-A and FL-SCSI-B) to reduce the efforts required by channel estimations. In FL-SCSI-A, precoders adjust phases of transmitted signals with phases of instant channel state information (CSI), and scale transmitted signal powers based on statistical CSI. The precoder design has the following two advantages. First, phases of instant CSI can be easier to estimate than complete instant CSI. Second, since clients only need to estimate phases of their own instant CSI (instead of CSI of all clients), with channel reciprocity, this can be easily achieved by letting the server broadcast pilots to all clients. The server in FL-SCSI-A is also efficient. It only needs to estimate the sum of channel gains of all clients, which can be easily achieved by letting clients transmit pilots simultaneously. To further reduce the communication overhead, FL-SCSI-B is proposed. The precoders in FL-SCSI-B are similar to FL-SCSI-A, while the server does not require any knowledge of instant CSI, which reduces the demands of channel estimations. For both schemes, we prove that the distortion caused by the noisy fading MAC is bounded, and the convergences of the learning processes are guaranteed for strongly smooth losses with heterogeneous data assumptions. Experimental results show that the proposed schemes perform better than benchmark schemes while reducing efforts required by channel estimation.
Overcoming the Coordination Problem in New Marketplaces via Cryptographic Tokens

New platforms such as peer-to-peer marketplaces frequently face a “coordination problem” in attracting users, as potential users may be reluctant to join given the uncertainty of other users joining, and this could cause the new platform to fail. There are several mechanisms in the literature to help address this coordination problem, such as subsidies for early users and promises of refunds or buybacks if the platform fails; these mechanisms, however, are likely to favor incumbent and larger firms with established reputation and financial resources. Platform-specific tradable cryptographic tokens allow a platform such as a marketplace to trade future revenue for present revenue, which then can be used to address the coordination problem. If the new platform is capital-constrained, as is often the case for new entrants and unproven technology applications, then tokens can offer an attractive alternative. As this benefit results from the tradability of the tokens, regulators and policy makers should facilitate or at least not unduly constrain the issuance of tradable utility tokens by new platforms. There is no corresponding benefit from user anonymity, and thus no corresponding drawback from identity regulations to establish accountability.
Practical Private Aggregation in Federated Learning Against Inference Attack

Federated learning (FL) enables multiple worker devices share local models trained on their private data to collaboratively train a machine learning model. However, local models are proved to imply the information about the private data and, thus, introduce much vulnerabilities to inference attacks where the adversary reconstructs or infers the sensitive information about the private data (e.g., labels, memberships, etc.) from the local models. To address this issue, existing works proposed homomorphic encryption, secure multiparty computation (SMC), and differential privacy methods. Nevertheless, the homomorphic encryption and SMC-based approaches are not applicable to large-scale FL scenarios as they incur substantial additional communication and computation costs and require secure channels to delivery keys. Moreover, differential privacy brings a substantial tradeoff between privacy budget and model performance. In this article, we propose a novel FL framework, which can protect the data privacy of worker devices against the inference attacks with minimal accuracy cost and low computation and communication cost, and does not rely on the secure pairwise communication channels. The main idea is to generate the lightweight keys based on computational Diffie–Hellman (CDH) problem to encrypt the local models, and the FL server can only get the sum of the local models of all worker devices without knowing the exact local model of any specific worker device. The extensive experimental results on three real-world data sets validate that the proposed FL framework can protect the data privacy of worker devices, and only incurs a small constant of computation and communication cost and a drop in test accuracy of no more than 1%.
Decision fusion of two sensors object classification based on the evidential reasoning rule

• The method proposed can deal with two sensors fusion problem. • The reliability reflects the environment influence of the sensor. • The weight reflects detection ability of the sensor. • The reliability is described by the difference among decisions in the time span. • The weight is described by the degree of dispersion of classification scores. The application of multiple sensors for object classification is commonly used in unmanned intelligent systems such as autonomous driving and robots. Current researches based on evidential reasoning rules mainly consider multiple sensor fusion. The reliability is determined by the decisions of the majority of sensors and the weight is determined by historical accuracy. However, the existing researches using the evidential reasoning rule are not applicable when there are only two sensors working in some situations. This paper proposes a two sensors decision fusion method for object classification based on the evidential reasoning rule. The reliability of sensors is calculated with the Logistic model based on the differences of the classification decisions in a certain time span and the adaptive weights of sensors are calculated based on the coefficient of variation to adapt to different environments. The final decision is drawn out by fusing the classification decisions of two sensors based on the fusion rules of evidential reasoning rule. Comparative experimental studies of object classification decision fusion in autonomous driving are carried out with the Nuscenes dataset and the Waymo dataset. The results show that the method proposed can effectively improve the accuracy of the fusion when there are only two sensors.
Evaluating Students' Perceptions of Online Learning with 2-D Virtual Spaces

The COVID-19 pandemic led the majority of educational institutions to rapidly shift to primarily conducting courses through online, remote delivery. Across different institutions, the tools used for synchronous online course delivery varied. They included traditional video conferencing tools like Zoom, Google Meet, and WebEx as well as non-traditional tools like Gather.Town, Gatherly, and YoTribe. The main distinguishing characteristic of these nontraditional tools is their utilization of 2-D maps to create virtual meeting spaces that mimic real-world spaces.
Micro-credentials: A Postdigital Counternarrative

Abstract Alternative credentials, such as micro credentials and digital badges, are increasingly being developed in universities. Primarily aimed at employability, these credentials are being scrutinised as it is argued that they are surrounded by ‘great uncertainty’. We contribute to this scrutiny by examining the forms of subjectivity that result from micro-credentials. We argue that, in their current form, they tend to result in a neoliberal subject. Presented is a counter-narrative that examines the current state of micro-credentials and offers an alternative form that refuses instrumentalist logics. In doing so, we draw from Foucault and postdigital research. The result is a recommendation to implement three principles in the design of micro-credentials. The first is the principle of being embedded in the curriculum, the second is alignment with the university mission, and the third is a critical and reflective pedagogy. This recommendation offers further possible subjectivities.
Improving Phishing Reporting Using Security Gamification

Phishing is an increasing threat that causes billions in losses and damage to productivity, trade secrets, and reputations each year. This work explores how security gamification techniques can improve phishing reporting. We contextualized the cognitive evaluation theory (CET) as a kernel theory and constructed a prototype phishing reporting system. With three experiments in a simulated work setting, we tested gamification elements of validation, attribution, incentives, and public presentation for improvements in experiential (e.g., motivation) and instrumental outcomes (e.g., hits and false positives) in phishing reporting. Our findings suggest public attribution with rewards and punishments best balance the competing necessities of accuracy with widespread reporting. Furthermore, our results demonstrate the unique benefits of security gamification to phishing reporting over and above other phishing mitigation techniques (e.g., training and warnings). However, we also noted that unintended consequences in false alarms might arise from shifts in motivation resulting from public display of incentives. These findings suggest that carefully calibrated external incentives (rather than intrinsic rewards) are most likely to improve the ancillary task of phishing reporting.
Issues, risks, and challenges for auditing crypto asset transactions

Many entities are progressively engaged in crypto asset transactions. The distinct nature of crypto assets from typical financial instruments makes it more challenging for external auditors to provide reasonable assurance on financial statements encompassing material crypto asset activities and transactions. To provide more specific guidance in crypto asset-related audits, this paper aims to (1) identify various participants in the crypto asset ecosystem and illustrate their relationship with the audited entity, (2) identify and elaborate the new challenges and risks for financial statement audits related to the crypto asset ecosystem, and (3) summarize issues to be considered in crypto asset-related audits in an audit framework. The dynamically evolving crypto asset ecosystem not only brings challenges and risks but also new assurance opportunities to the auditing profession after identifying and addressing those critical issues.
Safe-Control-Gym: A Unified Benchmark Suite for Safe Learning-Based Control and Reinforcement Learning in Robotics

In recent years, both reinforcement learning and learning-based control—as well as the study of their <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">safety</i> , which is crucial for deployment in real-world robots—have gained significant traction. However, to adequately gauge the progress and applicability of new results, we need the tools to equitably compare the approaches proposed by the controls and reinforcement learning communities. Here, we propose a new open-source benchmark suite, called <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">safe-control-gym</monospace> , supporting both model-based and data-based control techniques. We provide implementations for three dynamic systems—the cart-pole, the 1D, and 2D quadrotor—and two control tasks—stabilization and trajectory tracking. We propose to extend OpenAI's <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Gym</i> API—the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">de facto</i> standard in reinforcement learning research—with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">(i)</i> the ability to specify (and query) symbolic dynamics and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">(ii)</i> constraints, and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">(iii)</i> (repeatably) inject simulated disturbances in the control inputs, state measurements, and inertial properties. To demonstrate our proposal and in an attempt to bring research communities closer together, we show how to use <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">safe-control-gym</monospace> to quantitatively compare the control performance, data efficiency, and safety of multiple approaches from the fields of traditional control, learning-based control, and reinforcement learning.
A Comparative Study of Consensus Mechanisms in Blockchain for IoT Networks

The consensus mechanism is a core component of Blockchain technology, allowing thousands of nodes to agree on a single and consistent view of the Blockchain. A carefully selected consensus mechanism can provide attributes such as fault tolerance and immutability to an application. The Internet of Things (IoT) is a use case that can take advantage of these unique Blockchain properties. IoT devices are commonly implemented in sensitive domains such as health, smart cities, and supply chains. Resilience and data integrity are important for these domains, as failures and malicious data tampering could be detrimental to the systems that rely on these IoT devices. Additionally, Blockchains are well suited for decentralised networks and networks with high churn rates. A difficulty involved with applying Blockchain technology to the IoT is the lack of computational resources. This means that traditional consensus mechanisms like Proof of Work (PoW) are unsuitable. In this paper, we will compare several popular consensus mechanisms using a set of criteria, with the aim of understanding which consensus mechanisms are suitable for deployment in the IoT, and what trade-offs are required. We show that there are opportunities for both PoW and PoS to be implemented in the IoT, with purpose-made IoT consensus mechanisms like PoSCS and Microchain. Our analysis shows that Microchain and PoSCS have characteristics that are well suited for IoT consensus.
Online and offline streaming feature selection methods with bat algorithm for redundancy analysis

Streaming feature selection (SFS), is the task of selecting the most informative features in dealing with high-dimensional or incrementally growing problems. Several SFS algorithms have been proposed in the literature. However, they do not consider all feature subsets at the redundancy analysis step due to computational concerns. Moreover, they do not reconsider previously removed features which leads to losing most of the useful information. In this paper, the redundancy analysis step is defined as a binary optimization problem. Then, a binary bat algorithm (BBA) is adopted to find the minimal informative subsets. In this way, a large number of feature subsets can be considered effectively at the redundancy analysis step. In addition, an effective priority list is used to maintain previously removed redundant features. Such a list allows the re-examination of informative features. As a result, it is possible to consider the mutual information between features that are not streamed in an small time interval. Experimental studies on fifteen different types of datasets show that our approach is superior to state-of-the-art online and offline streaming feature selection methods in terms of classification accuracy.
HSGM: A Hierarchical Similarity Graph Module for Object Re-Identification

Existing object re-identification methods usually utilize backbone networks developed based on classification tasks to obtain the final object features. However, these backbone networks lack a unique mechanism to explore discriminative feature representation and handle rich scale changes. For that, a novel hierarchical similarity graph module (HSGM) is proposed to relieve the conflict of backbone networks and mine the discriminative features. Specifically, the proposed HSGM constructs a rich hierarchical graph to explore the pairwise relationships among global-local and local-local. Then, in each hierarchical graph, the HSGM regards local features extracted from different locations as nodes and utilizes the similarity scores between nodes to construct a similarity graph. During the HSGM's propagation, a learnable parameter is reweighted at each spatial position to optimize the correlation between adjacent nodes. Besides, the HSGM can be readily inserted into backbone networks at any depth to improve object discrimination. Extensive experiments on two large-scale object datasets (i.e., VeRi776 and Market-1501) demonstrate that the proposed HSGM is superior to state-of-the-art object re-identification approaches.
Wavelet-Based CNN for Robust and High-Capacity Image Watermarking

“Physical” watermarking, i.e., print-scanning, print-camera, and screen-shooting resilient watermarking, has drawn great attention these years. Recent studies show that watermarking with deep networks, e.g., StegaStamp, is particularly suitable for these tasks with strong robustness against the attacks from “physical transmissions” in the wild, although it usually has a relatively small amount of embedding capacity. Recognizing the conventional CNNs are vulnerable to input noise inter-ruptions, the wavelet-based CNNs are adopted in our work by replacing their down-sampling (pooling) and up-sampling layers with Discrete Wavelet Transform (DWT) and Inverse Discrete Wavelet Transform (IDWT), respectively, to learn the stable feature representation from noise-corrupted sam-ples. A new residual regularization loss function incorporating the texture complexity is also proposed to significantly improve the visual quality of watermarked images. Exper-imental results show that the proposed wavelet-based CNN model significantly outperforms the state-of-the-art StegaS-tamp in terms of embedding capacity, imperceptibility, and robustness.
Learning joint relationship attention network for image captioning

Image captioning aims at automatically describing the main content of an image with a complete and natural sentence. Existing attention-based methods often focus on visual features individually, while ignoring relationship information between image features that provides important guidance for generating captions. To alleviate this issue, in this work we propose the new Joint Relationship Attention Network (JRAN) that novelly explores the relationships between the features in the image. Technically, JRAN capitalizes on semantic features as s supplementary to the region features, fully learn two types of relationships, the visual relationships between region features and the visual–semantic relationships between region features and semantic features. Then, JRAN further make a dynamic trade-off between them during outputting the relationship representation. Moreover, we devise a new feature fusion based attention, which can adaptively fuse the region features and previously obtained relationship representation when generating different words. Extensive experiments on MSCOCO and Flickr30k, Flickr8k datasets show the superiority of our JRAN method qualitatively and quantitatively compared with several related state-of-the-art methods. More remarkably, JRAN achieves 28.3% and 58.2% on Flickr30k, and 22.7% and 55.3% on Flickr8k for the metrics BLEU4 and CIDEr.