Reviewerly/exHarmony huggingface dataset:
Considerations for Neuromorphic Supercomputing in Semiconducting and Superconducting Optoelectronic Hardware

Any large-scale spiking neuromorphic system striving for complexity at the level of the human brain and beyond will need to be co-optimized for communication and computation. Such reasoning leads to the proposal for optoelectronic neuromorphic platforms that leverage the complementary properties of optics and electronics. Starting from the conjecture that future large-scale neuromorphic systems will utilize integrated photonics and fiber optics for communication in conjunction with analog electronics for computation, we consider two possible paths toward achieving this vision. The first is a semiconductor platform based on analog CMOS circuits and waveguide-integrated photodiodes. The second is a superconducting approach that utilizes Josephson junctions and waveguide-integrated superconducting single-photon detectors. We discuss available devices, assess scaling potential, and provide a list of key metrics and demonstrations for each platform. Both platforms hold potential, but their development will diverge in important respects. Semiconductor systems benefit from a robust fabrication ecosystem and can build on extensive progress made in purely electronic neuromorphic computing but will require III-V light source integration with electronics at an unprecedented scale, further advances in ultra-low capacitance photodiodes, and success from emerging memory technologies. Superconducting systems place near theoretically minimum burdens on light sources (a tremendous boon to one of the most speculative aspects of either platform) and provide new opportunities for integrated, high-endurance synaptic memory. However, superconducting optoelectronic systems will also contend with interfacing low-voltage electronic circuits to semiconductor light sources, the serial biasing of superconducting devices on an unprecedented scale, a less mature fabrication ecosystem, and cryogenic infrastructure.
LiDARTouch: Monocular metric depth estimation with a few-beam LiDAR

Vision-based depth estimation is a key feature in autonomous systems, which often relies on a single camera or several independent ones. In such a monocular setup, dense depth is obtained with either additional input from one or several expensive LiDARs, e.g., with 64 beams, or camera-only methods, which suffer from scale-ambiguity and infinite-depth problems. In this paper, we propose a new alternative of densely estimating metric depth by combining a monocular camera with a light-weight LiDAR, e.g., with 4 beams, typical of today's automotive-grade mass-produced laser scanners. Inspired by recent self-supervised methods, we introduce a novel framework, called LiDARTouch, to estimate dense depth maps from monocular images with the help of "touches" of LiDAR, i.e., without the need for dense ground-truth depth. In our setup, the minimal LiDAR input contributes on three different levels: as an additional model's input, in a self-supervised LiDAR reconstruction objective function, and to estimate changes of pose (a key component of self-supervised depth estimation architectures). Our LiDARTouch framework achieves new state of the art in self-supervised depth estimation on the KITTI dataset, thus supporting our choices of integrating the very sparse LiDAR signal with other visual features. Moreover, we show that the use of a few-beam LiDAR alleviates scale ambiguity and infinite-depth issues that camera-only methods suffer from. We also demonstrate that methods from the fully-supervised depth-completion literature can be adapted to a self-supervised regime with a minimal LiDAR signal.
Decoding of Quantum Data-Syndrome Codes via Belief Propagation

Quantum error correction is necessary to protect logical quantum states and operations. However, no meaningful data protection can be made when the syndrome extraction is erroneous due to faulty measurement gates. Quantum data-syndrome (DS) codes are designed to protect the data qubits and syndrome bits concurrently. In this paper, we propose an efficient decoding algorithm for quantum DS codes with sparse check matrices. Based on a refined belief propagation (BP) decoding for stabilizer codes, we propose a DS-BP algorithm to handle the quaternary quantum data errors and binary syndrome bit errors. Moreover, a sparse quantum code may inherently be able to handle minor syndrome errors so that fewer redundant syndrome measurements are necessary. We demonstrate this with simulations on a quantum hypergraph-product code.
Members of Parliament in Finland Knowledge Graph and Its Linked Open Data Service

This paper presents a prosopographical knowledge graph describing the Members of Parliament in Finland and related actors in politics, extracted from the databases and textual descriptions of the Parliament of Finland. The data has been interlinked internally and enriched with data linking to external data sources according to the 5-star Linked Data model. The data has been published together with its schema for better re-usability and is validated using ShEx. The knowledge graph presented is integrated with another knowledge graph about over 900 000 parliamentary plenary speeches in Finland (1907–) to form a larger parliamentary LOD publication FinnParla of the Parliament of Finland. The data is being used for Digital Humanities research on parliamentary networks, culture, and language.
Morphence: Moving Target Defense Against Adversarial Examples

Robustness to adversarial examples of machine learning models remains an open topic of research. Attacks often succeed by repeatedly probing a fixed target model with adversarial examples purposely crafted to fool it. In this paper, we introduce Morphence, an approach that shifts the defense landscape by making a model a moving target against adversarial examples. By regularly moving the decision function of a model, Morphence makes it significantly challenging for repeated or correlated attacks to succeed. Morphence deploys a pool of models generated from a base model in a manner that introduces sufficient randomness when it responds to prediction queries. To ensure repeated or correlated attacks fail, the deployed pool of models automatically expires after a query budget is reached and the model pool is seamlessly replaced by a new model pool generated in advance. We evaluate Morphence on two benchmark image classification datasets (MNIST and CIFAR10) against five reference attacks (2 white-box and 3 black-box). In all cases, Morphence consistently outperforms the thus-far effective defense, adversarial training, even in the face of strong white-box attacks, while preserving accuracy on clean data and reducing attack transferability.
Tighter monogamy and polygamy relations for a superposition of the generalized W-class state and vacuum

Monogamy and polygamy relations characterize the distributions of entanglement in multipartite systems. We investigate the monogamy and polygamy relations with respect to any partitions for a superposition of the generalized $W$-class state and vacuum in terms of the Tsallis-$q$ entanglement and the R\'enyi-$\alpha$ entanglement. By using the Hamming weight of the binary vectors related to the partitions of the subsystems, new classes of monogamy and polygamy inequalities are derived, which are shown to be tighter than the existing ones. Detailed examples are presented to illustrate the finer characterization of entanglement distributions.
Efficient steady-state-entanglement generation in strongly driven coupled qubits

We report on a mechanism to optimize the generation of steady-state entanglement in a system of coupled qubits driven by microwave fields. Due to the interplay between Landau-Zener-St\"uckerlberg pumping involving three levels and a subsequent fast relaxation channel, which is activated by tuning the qubits-reservoir couplings, a maximally entangled state can be populated. This mechanism does not require from the fine-tuning of multiphoton-resonances but depends on the sign of the qubit-qubit coupling. In particular, we find that by a proper design of the system parameters and the driving protocol, the two-qubits steady-state concurrence can attain values close to 1 in a wide range of driving amplitudes. Our results may be useful to gain further insight into entanglement control and manipulation in dissipative quantum systems exposed to strong driving.
A Search Engine for Discovery of Scientific Challenges and Directions

Keeping track of scientific challenges, advances and emerging directions is a fundamental part of research. However, researchers face a flood of papers that hinders discovery of important knowledge. In biomedicine, this directly impacts human lives. To address this problem, we present a novel task of extraction and search of scientific challenges and directions, to facilitate rapid knowledge discovery. We construct and release an expert-annotated corpus of texts sampled from full-length papers, labeled with novel semantic categories that generalize across many types of challenges and directions. We focus on a large corpus of interdisciplinary work relating to the COVID-19 pandemic, ranging from biomedicine to areas such as AI and economics. We apply a model trained on our data to identify challenges and directions across the corpus and build a dedicated search engine. In experiments with 19 researchers and clinicians using our system, we outperform a popular scientific search engine in assisting knowledge discovery. Finally, we show that models trained on our resource generalize to the wider biomedical domain and to AI papers, highlighting its broad utility. We make our data, model and search engine publicly available.
Several classes of linear codes with few weights from the closed butterfly structure

Linear codes with few weights have applications in data storage systems, secret sharing schemes and authentication codes. In this paper, inspired by the butterfly structure [6], [29] and the works of Li, Yue and Fu [21] and Jian, Lin and Feng [19], we introduce a new defining set with the form of the closed butterfly structure and consequently we obtain three classes of 3-weight binary linear codes and a class of 4-weight binary linear codes whose dual is optimal. The lengths and weight distributions of these four classes of linear codes are completely determined by some detailed calculations on certain exponential sums. Computer experiments show that many (almost) optimal codes can be obtained from our construction.
Optimizing for Interpretability in Deep Neural Networks with Tree Regularization

Deep models have advanced prediction in many domains, but their lack of interpretability remains a key barrier to the adoption in many real world applications. There exists a large body of work aiming to help humans understand these black box functions to varying levels of granularity – for example, through distillation, gradients, or adversarial examples. These methods however, all tackle interpretability as a separate process after training. In this work, we take a different approach and explicitly regularize deep models so that they are well-approximated by processes that humans can step through in little time. Specifically, we train several families of deep neural networks to resemble compact, axis-aligned decision trees without significant compromises in accuracy. The resulting axis-aligned decision functions uniquely make tree regularized models easy for humans to interpret. Moreover, for situations in which a single, global tree is a poor estimator, we introduce a regional tree regularizer that encourages the deep model to resemble a compact, axis-aligned decision tree in predefined, human-interpretable contexts. Using intuitive toy examples, benchmark image datasets, and medical tasks for patients in critical care and with HIV, we demonstrate that this new family of tree regularizers yield models that are easier for humans to simulate than L1 or L2 penalties without sacrificing predictive power.
Cryptanalysis of a white‐box SM4 implementation based on collision attack

White-box cryptography is to primarily protect the key of a cipher from being extracted in a white-box scenario, where an adversary has full access to the execution environment of software implementation. Since the introduction of white-box cryptography, a number of white-box implementations of the Chinese SM4 block cipher standard have been proposed, and all of them have been attacked based on Billet et al.’s attack. In this study, we show that collision-based attack can work more efficiently on Shi et al.’s white-box SM4 implementation than the previously published attacks, by devising an attack with a time complexity of 2 23 , significantly reducing the previously known time complexity of 2 49 to a very practical level. Our attack can also be similarly applied to some other white-box SM4 implementations.
The Effect of Noise on the Performance of Variational Algorithms for Quantum Chemistry

Variational quantum algorithms are suitable for use on noisy quantum systems. One of the most important use-cases is the quantum simulation of materials, using the variational quantum eigensolver (VQE). To optimize VQE performance, a suitable parameterized quantum circuit (ansatz) must be selected. We investigate a class of ansatze that incorporates knowledge of the quantum hardware, namely the hardware efficient ansatze. The performance of hardware efficient ansatze is affected differently by noise, and our goal is to study the effect of noise on evaluating which ansatz gives more accurate results in practice. First, we study the effect of noise on the different hardware efficient ansatze by benchmarking and ranking the performance of each ansatz family (i) on a chemistry application using VQE and (ii) by the recently established metric of "expressibility". The results demonstrate the ranking of optimal circuits does not remain constant in the presence of noise. Second, we evaluate the suitability of the expressibility measure in this context by performing a correlation study between expressibility and the performance of the same circuits on a chemistry application using VQE. Our simulations reveal a weak correlation and therefore demonstrate that expressibility is not an adequate measure to quantify the effectiveness of parameterized quantum circuits for quantum chemistry. Third, we evaluate the effect of different quantum device noise models on the ordering of which ansatz family is best. Interestingly, we see that to decide which ansatz is optimal for use, one needs to consider the specific hardware used even within the same family of quantum hardware.
DRLAS: Digital Record Keeping in Land Administration System Relying on Blockchain

On every developed or developing world, the Land Administration System (LAS) is a salient infrastructure. Both the digital (traditional database system) and the manual methods are applied in the LAS (paper-based documentations). Both of these systems provide an authority with monopoly power that can increase unethical conduct in land administration. Again, while the Blockchain technology was initially used to maintain a financial ledger, it is possible to expand the use of this technology to incorporate any decentralized computing structures, including the automated record keeping and management framework. Recent studies have found that the new and largely untested applications of Blockchain technology in land administration remain. In this work, the implementation of a blockchain-based system is proposed to build a System for Land Administration. In the Ethereum blockchain platform, the proposed framework was simulated and showed that the framework contributed to developing a stable, secure, effective and efficient system of land administration.
Combination of PCA with SMOTE Oversampling for Classification of High-Dimensional Imbalanced Data

Imbalanced data classification is a common issue in data mining where the classifiers are skewed towards the larger data class. Classification of high-dimensional skewed (imbalanced) data is of great interest to decision-makers as it is more difficult to. The dimension reduction method, a process in which variables are reduced, allows high dimensional datasets to be interpreted more easily with a certain loss. This study, a method combining SMOTE oversampling with principal component analysis is proposed to solve the imbalance problem in high dimensional data. Three classification algorithms consisting of Logistic Regression, K-Nearest Neighbor, Decision Tree methods and two separate datasets were utilized to evaluate the suggested method's efficacy and determine the classifiers' performance. Respectively, raw datasets, converted datasets by PCA, SMOTE and SMOTE+PCA (SMOTE and PCA) methods, were analyzed with the given algorithms. Analyzes were made using WEKA. Analysis results suggest that almost all classification algorithms improve their classification performance using PCA, SOMTE, and SMOTE+PCA methods. However, the SMOTE method gave more efficient results than PCA and PCA+SMOTE methods for data rebalancing. Experimental results also suggest that K-Nearest Neighbor classifier provided higher classification performance compared to other algorithms.
Dependence in elliptical partial correlation graphs

The Gaussian model equips strong properties that facilitate studying and interpreting graphical models. Specifically it reduces conditional independence and the study of positive association to determining partial correlations and their signs. When Gaussianity does not hold partial correlation graphs are a useful relaxation of graphical models, but it is not clear what information they contain (besides the obvious lack of linear association). We study elliptical and transelliptical distributions as middle-ground between the Gaussian and other families that are more flexible but either do not embed strong properties or do not lead to simple interpretation. We characterize the meaning of zero partial correlations in elliptical and elliptical copula models and show that they retain much of the dependence structure from the Gaussian case. Regarding positive dependence, we prove impossibility results to learn certain positive (trans)elliptical graphical models, including that an elliptical distribution that is multivariate totally positive of order two for all dimensions must be essentially Gaussian. We then show how to interpret positive partial correlations as a relaxation, and obtain important properties related to faithfulness and Simpson’s paradox. We illustrate the transelliptical model potential to study tail dependence in S&P500 data, and of positivity to improve regularized inference.
Safe MQTT-SN: a lightweight secure encrypted communication in IoT

Abstract The Internet of Things (IoT) is a technology trend that has been evolving in recent years. With the development of sensing technology and communication technology, the various applications of IoT are integrated into our lives gradually. Various security issues also need to be resolved with the convenience brought to us by IoT. MQTT or MQTT-SN is a lightweight communication protocol in IoT, which is more compactly on sensor nodes with limited resources. This paper provides a secure connection and end-to-end encrypted communication method which is based on MQTT-SN. By using digital signature (ECDSA), hash function, key exchange (ECDHE), AEAD (ChaCha20-Poly1305) to achieve a safe version of MQTT-SN called Safe MQTT-SN. The Safe MQTT-SN enables an end-to-end encrypted communication between the subscriber and publisher. The implementation of these algorithms enable the Safe MQTT-SN to have good performance on sensor nodes with limited computing power. Comparing with mosquito MQTT-TLS, the handshake time of MQTT-TLS is about 56.12ms and Safe MQTT-SN is about 24.78ms on Zedboard.
Evaluation of Green Alternatives for Blockchain Proof-of-Work (PoW) Approach

Following the footprints of Bitcoins, many other cryptocurrencies were developed mostly adopting the same or similar Proof-of-Work (PoW) approach. Since completing the PoW puzzle requires extremely high computing power, consuming a vast amount of electricity, PoW has been strongly criticised for its antithetic stand against the notion of green computing. Use of application-specific hardware, particularly application-specific integrated circuits (ASICs) has further fuelled the debate, as these devices are of no use once they become “legacy” and hence obsolete to compete in the mining race, thus contributing to electronics waste. Therefore, this paper surveys the currently available alternative approaches to PoW and evaluates their applicability - especially their appropriateness in terms of greenness.
Generating contrastive explanations for inductive logic programming based on a near miss approach

Abstract In recent research, human-understandable explanations of machine learning models have received a lot of attention. Often explanations are given in form of model simplifications or visualizations. However, as shown in cognitive science as well as in early AI research, concept understanding can also be improved by the alignment of a given instance for a concept with a similar counterexample. Contrasting a given instance with a structurally similar example which does not belong to the concept highlights what characteristics are necessary for concept membership. Such near misses have been proposed by Winston (Learning structural descriptions from examples, 1970) as efficient guidance for learning in relational domains. We introduce an explanation generation algorithm for relational concepts learned with Inductive Logic Programming ( GeNME ). The algorithm identifies near miss examples from a given set of instances and ranks these examples by their degree of closeness to a specific positive instance. A modified rule which covers the near miss but not the original instance is given as an explanation. We illustrate GeNME with the well-known family domain consisting of kinship relations, the visual relational Winston arches domain, and a real-world domain dealing with file management. We also present a psychological experiment comparing human preferences of rule-based, example-based, and near miss explanations in the family and the arches domains.
Auditing Hamiltonian Elections

Presidential primaries are a critical part of the United States Presidential electoral process, since they are used to select the candidates in the Presidential election. While methods differ by state and party, many primaries involve proportional delegate allocation using the so-called Hamilton method. In this paper we show how to conduct risk-limiting audits for delegate allocation elections using variants of the Hamilton method where the viability of candidates is determined either by a plurality vote or using instant runoff voting. Experiments on real-world elections show that we can audit primary elections to high confidence (small risk limits) usually at low cost.