Reviewerly/exHarmony huggingface dataset:
A Supervised Multi-class Multi-label Word Embeddings Approach for Toxic Comment Classification

Nowadays, communications made by using the modern Internet-based opportunities have revolutionized the way people exchange information, allowing real-time discussions among a huge number of users. However, the advantages offered by such powerful instruments of communication are sometimes jeopardized by the dangers related to personal attacks that lead many people to leave a discussion that they were participating. Such a problem is related to the so-called toxic comments, i.e., personal attacks, verbal bullying and, more generally, an aggressive way in which many people participate in a discussion, which brings some participants to abandon it. By exploiting the Apache Spark big data framework and several word embeddings, this paper presents an approach able to operate a multi-class multi-label classification of a discussion within a range of six classes of toxicity. We evaluate such an approach by classifying a dataset of comments taken from the Wikipedia’s talk page, according to a Kaggle challenge. The experimental results prove that, through the adoption of different sets of word embeddings, our supervised approach outperforms the state-of-the-art that operate by exploiting the canonical bag-of-word model. In addition, the adoption of a word embeddings defined in a similar scenario (i.e., discussions related to e-learning videos), proves that it is possible to improve the performance with respect to solutions employing state-of-the-art word embeddings.
A Novel CCA Attack Using Decryption Errors Against LAC

Cryptosystems based on Learning with Errors or related problems are central topics in recent cryptographic research. One main witness to this is the NIST Post-Quantum Cryptography Standardization effort. Many submitted proposals rely on problems related to Learning with Errors. Such schemes often include the possibility of decryption errors with some very small probability. Some of them have a somewhat larger error probability in each coordinate, but use an error correcting code to get rid of errors. In this paper we propose and discuss an attack for secret key recovery based on generating decryption errors, for schemes using error correcting codes. In particular we show an attack on the scheme LAC, a proposal to the NIST Post-Quantum Cryptography Standardization that has advanced to round 2. In a standard setting with CCA security, the attack first consists of a precomputation of special messages and their corresponding error vectors. This set of messages are submitted for decryption and a few decryption errors are observed. In a statistical analysis step, these vectors causing the decryption errors are processed and the result reveals the secret key. The attack only works for a fraction of the secret keys. To be specific, regarding LAC256, the version for achieving the 256-bit classical security level, we recover one key among approximately $$2^{64}$$ public keys with complexity $$2^{79}$$, if the precomputation cost of $$2^{162}$$ is excluded. We also show the possibility to attack a more probable key (say with probability $$2^{-16}$$). This attack is verified via extensive simulation. We further apply this attack to LAC256-v2, a new version of LAC256 in round 2 of the NIST PQ-project and obtain a multi-target attack with slightly increased precomputation complexity (from $$2^{162}$$ to $$2^{171}$$). One can also explain this attack in the single-key setting as an attack with precomputation complexity of $$2^{171}$$ and success probability of $$2^{-64}$$.
Deep Learning for Symbolic Mathematics

Neural networks have a reputation for being better at solving statistical or approximate problems than at performing calculations or working with symbolic data. In this paper, we show that they can be surprisingly good at more elaborated tasks in mathematics, such as symbolic integration and solving differential equations. We propose a syntax for representing mathematical problems, and methods for generating large datasets that can be used to train sequence-to-sequence models. We achieve results that outperform commercial Computer Algebra Systems such as Matlab or Mathematica.
Overlearning Reveals Sensitive Attributes

``Overlearning'' means that a model trained for a seemingly simple objective implicitly learns to recognize attributes and concepts that are (1) not part of the learning objective, and (2) sensitive from a privacy or bias perspective. For example, a binary gender classifier of facial images also learns to recognize races, even races that are not represented in the training data, and identities. We demonstrate overlearning in several vision and NLP models and analyze its harmful consequences. First, inference-time representations of an overlearned model reveal sensitive attributes of the input, breaking privacy protections such as model partitioning. Second, an overlearned model can be `re-purposed'' for a different, privacy-violating task even in the absence of the original training data. We show that overlearning is intrinsic for some tasks and cannot be prevented by censoring unwanted attributes. Finally, we investigate where, when, and why overlearning happens during model training.
Embedding and Predicting Software Security Entity Relationships: A Knowledge Graph Based Approach

Software security knowledge involves heterogeneous security concepts (e.g., software weaknesses and attack patterns) and security instances (e.g., the vulnerabilities of a particular software product), which can be regarded as software security entities. Among software security entities, there are many within-type relationships as well as many across-type relationships. Predicting software security entity relationships helps to enrich software security knowledge (e.g., finding missing relationships among existing entities). Unfortunately, software security entities are currently documented in separate databases, such as Common Vulnerabilities and Exposures (CVE), Common Weakness Enumeration (CWE) and Common Attack Pattern Enumeration and Classification (CAPEC). This hyper-document representation cannot support effective reasoning of software entity relationships. In this paper, we propose to consolidate heterogeneous software security concepts and instances from separate databases into a coherent knowledge graph. We develop a knowledge graph embedding method which embeds the symbolic relational and descriptive information of software security entities into a continuous vector space. The resulting entity and relationship embeddings are predictive for software security entity relationships. Based on the Open World Assumption, we conduct extensive experiments to evaluate the effectiveness of our knowledge graph based approach for predicting various within-type and across-type relationships of software security entities.
Multi-qubit phase gate on multiple resonators mediated by a superconducting bus

We propose a one-step scheme for implementing multi-qubit phase gates on microwave photons in multiple resonators mediated by a superconducting bus in circuit quantum electrodynamics (QED) system. In the scheme, multiple single-mode resonators carry quantum information with their vacuum and single-photon Fock states, and a multi-level artificial atom acts as a quantum bus which induces the indirect interaction among resonators. The method of pulse engineering is used to shape the coupling strength between resonators and the bus so as to improve the fidelity and robustness of the scheme. We also discuss the influence of finite coherence time for the bus and resonators on gate fidelity respectively. Finally, we consider the suppression of unwanted transitions and propose the method of optimized detuning compensation for offsetting unwanted transitions, showing the feasibility of the scheme within the current experiment technology.
An <i>N</i>-State Markovian Jumping Particle Swarm Optimization Algorithm

Optimization is an important research field, especially in engineering, physical sciences, and economics. The main purpose of optimization is to maximize the profit and minimize the cost of production as well as the loss of the system. Evolutionary computation algorithms, such as the genetic algorithm and the particle swarm optimization (PSO) algorithm have been successfully employed in solving various optimization problems. Owing to its application potential and promising performance in discovering the optimal solution, the PSO algorithm has been recognized as a powerful optimization technique and attracted an ever-increasing interest in the evolutionary computation community. In this article, a novel <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> -state Markovian jumping PSO (NS-MJPSO) algorithm is presented where the velocity updating equation is adjusted based on the state evolution governed by a Markov chain. The performance of the proposed NS-MJPSO algorithm is evaluated via some widely used mathematical benchmark functions. The experimental results demonstrate that the developed NS-MJPSO algorithm outperforms some currently popular PSO algorithms on the widely used benchmark functions.
CollabAR  Investigating the Mediating Role of Mobile AR Interfaces on Co-Located Group Collaboration

Mobile Augmented Reality (AR) technology is enabling new applications for different domains including architecture, education or medical work. As AR interfaces project digital data, information and models into the real world, it allows for new forms of collaborative work. However, despite the wide availability of AR applications, very little is known about how AR interfaces mediate and shape collaborative practices. This paper presents a study which examines how a mobile AR (M-AR) interface for inspecting and discovering AR models of varying complexity impacts co-located group practices. We contribute new insights into how current mobile AR interfaces impact co-located collaboration. Our results show that M-AR interfaces induce high mental load and frustration, cause a high number of context switches between devices and group discussion, and overall leads to a reduction in group interaction. We present design recommendations for future work focusing on collaborative AR interfaces.
Edge Computing Integrated with Blockchain Technologies

With the rapid increasing of the number of devices connected to the Internet of Things (IoTs), the traditional centralized cloud computing system is unable to satisfy the Quality of Service (QoS) for many applications, especially for areas with real-time, reliability and security. The edge computing as an extension of the cloud computing is introduced, which lies in its ability to transfer the sensitive data from cloud to the edge for increasing network security and to realize high frequency interaction and real-time transmission of data. However, that the edge servers maintain sensitive privacy information generates many important security issues for the edge computing network. Moreover, the data produced by IoT devices are separated into many parts and stored in different edges servers that are located in different locations, which is hard to guarantee data integrity due to data loss and incorrect data storage in edge servers. As the emergence of blockchain technologies, the various security problems and data integrity of the edge computing can be addressed by integrating blockchain technologies. In this paper, we present a comprehensive overview of edge computing integrated with blockchain technologies. Firstly, the blockchain technologies and the architecture of the edge computing are introduced. Secondly, the motivations and architecture of the edge computing integrated with blockchain are introduced. Thirdly, the related works about the edge computing integrated with blockchain that have been investigated are introduced. Finally, the research challenges are discussed.
PoolCoin: Toward a distributed trust model for miners' reputation management in blockchain

In blockchain, transactions between parties are regrouped into blocks, in order to be added to the blockchain's distributed ledger. Miners are nodes of the network that generate new blocks that meet the consensus protocol. Thus, when a miner adds a valid block to the distributed ledger, the miner is rewarded. Due to the difficulty of the problem to be solved by the miner in order to find a valid block, it becomes difficult to a single miner to gain rewards. Therefore, miners join mining pools, where the powers of miners are federated to ensure stable revenues for miners. In public blockchains, access to mining pools is not restricted, which makes mining pools vulnerable to considerable threats such as: block withholding (BWH) attacks and distributed denial of service (DDoS) attacks. In the present work, we a new blockchain named PoolCoin that manages reputation in mining pools. In addition, we provide a trust model for PoolCoin, inspired by the job market signaling model. The proposed PoolCoin blockchain allows pool managers to accept trusted miners in their mining pools, while miners are able to evaluate pool managers. The performance evaluation is conducted and the obtained simulation results are presented and discussed. In order to study the efficiency of the proposed trust model, a performance evaluation was provided. Thus, the model parameters' are optimized in order to detect and exclude misbehaving miners, while honest miners are maintained in the mining pool.
Flexible and Efficient Verifiable Computation on Encrypted Data

We consider the problem of verifiable and private delegation of computation [Gennaro et al. CRYPTO’10] in which a client stores private data on an untrusted server and asks the server to compute functions over this data. In this scenario we aim to achieve three main properties: the server should not learn information on inputs and outputs of the computation (privacy), the server cannot return wrong results without being caught (integrity), and the client can verify the correctness of the outputs faster than running the computation (efficiency). A known paradigm to solve this problem is to use a (non-private) verifiable computation (VC) to prove correctness of a homomorphic encryption (HE) evaluation on the ciphertexts. Despite the research advances in obtaining efficient VC and HE, using these two primitives together in this paradigm is concretely expensive. Recent work [Fiore et al. CCS’14, PKC’20] addressed this problem by designing specialized VC solutions that however require the HE scheme to work with very specific parameters; notably HE ciphertexts must be over \(\mathbb {Z}_q\) for a large prime q.In this work we propose a new solution that allows a flexible choice of HE parameters, while staying modular (based on the paradigm combining VC and HE) and efficient (the VC and the HE schemes are both executed at their best efficiency). At the core of our new protocol are new homomorphic hash functions for Galois rings. As an additional contribution we extend our results to support non-deterministic computations on encrypted data and an additional privacy property by which verifiers do not learn information on the inputs of the computation.
Intensity inhomogeneity correction of MRI images using InhomoNet

Intensity inhomogeneity is one of the major artifacts in magnetic resonance imaging (MRI). Bias field present in MRI images alters true pixel value and produces spurious varying pixel intensities. This artifact affects the diagnosis by radiologists in a detrimental manner and also degrades the performance of computer-aided diagnosis algorithms such as segmentation. The present work proposes a novel network called InhomoNet for intensity inhomogeneity correction of MRI image. The generator architecture of InhomoNet consists of a new multi-scale local information module at each encoder block that helps to capture features at multiple scales. The horizontal and vertical kernels help to reduce the problems like loss of neighborhood information, gridding issues caused due to large dilated convolution operations. The attention-driven skip connections in the generator network are utilized to transfer optimal semantic and spatial localization information from the encoder to decoder blocks. Further, the present work proposes two new losses functions, i.e. histogram correlation and 3D pixel loss. These losses help to realize pixel consistency across different regions of brain MRI. The inculcation of the L1 loss provides guidance to the upsampling process as it compares the prediction from each decoder block with the ground truth. The proposed method is evaluated on simulated and real MRI data. The comparative analysis with popular state-of-the-art methods depicts the ability of the proposed method to perform intensity inhomogeneity correction accurately.
A comparative study of high-productivity high-performance programming languages for parallel metaheuristics

Parallel metaheuristics require programming languages that provide both, high performance and a high level of programmability. This paper aims at providing a useful data point to help practitioners gauge the difficult question of whether to invest time and effort into learning and using a new programming language. To accomplish this objective, three productivity-aware languages (Chapel, Julia, and Python) are compared in terms of performance, scalability and productivity. To the best of our knowledge, this is the first time such a comparison is performed in the context of parallel metaheuristics. As a test-case, we implement two parallel metaheuristics in three languages for solving the 3D Quadratic Assignment Problem (Q3AP), using thread-based parallelism on a multi-core shared-memory computer. We also evaluate and compare the performance of the three languages for a parallel fitness evaluation loop, using four different test-functions with different computational characteristics. Besides providing a comparative study, we give feedback on the implementation and parallelization process in each language.
Stochastic Latent Residual Video Prediction

Designing video prediction models that account for the inherent uncertainty of the future is challenging. Most works in the literature are based on stochastic image-autoregressive recurrent networks, which raises several performance and applicability issues. An alternative is to use fully latent temporal models which untie frame synthesis and temporal dynamics. However, no such model for stochastic video prediction has been proposed in the literature yet, due to design and training difficulties. In this paper, we overcome these difficulties by introducing a novel stochastic temporal model whose dynamics are governed in a latent space by a residual update rule. This first-order scheme is motivated by discretization schemes of differential equations. It naturally models video dynamics as it allows our simpler, more interpretable, latent model to outperform prior state-of-the-art methods on challenging datasets.
Learning Dynamic Belief Graphs to Generalize on Text-Based Games

Playing text-based games requires skills in processing natural language and sequential decision making. Achieving human-level performance on text-based games remains an open challenge, and prior research has largely relied on hand-crafted structured representations and heuristics. In this work, we investigate how an agent can plan and generalize in text-based games using graph-structured representations learned end-to-end from raw text. We propose a novel graph-aided transformer agent (GATA) that infers and updates latent belief graphs during planning to enable effective action selection by capturing the underlying game dynamics. GATA is trained using a combination of reinforcement and self-supervised learning. Our work demonstrates that the learned graph-based representations help agents converge to better policies than their text-only counterparts and facilitate effective generalization across game configurations. Experiments on 500+ unique games from the TextWorld suite show that our best agent outperforms text-based baselines by an average of 24.2%.
Performance of Coupled-Cluster Singles and Doubles on Modern Stream Processing Architectures

We develop a new implementation of coupled-cluster singles and doubles (CCSD) optimized for the most recent graphical processing unit (GPU) hardware. We find that a single node with 8 NVIDIA V100 GPUs is capable of performing CCSD computations on roughly 100 atoms and 1300 basis functions in less than 1 day. Comparisons against massively parallel implementations of CCSD suggest that more than 64 CPU-based nodes (each with 16 cores) are required to match this performance.
Enhancing web service clustering using Length Feature Weight Method for service description document vector space representation

Due to the rapid growth of web services in repositories, discovering the requisite web service is becoming increasingly cumbersome task. It has raised the demand for efficient web service clustering algorithms. In service repositories, when related web services are stored in a clustered way, it enhances the web service discovery process by reducing search space and time. Many eminent researchers have worked in this field and used the Term Frequency – Inverse Document Frequency (TF-IDF) method for representing web services in vector space. In general, there are various limitations of the TF-IDF approach i.e. (1) Not efficient for large documents (2) Position of term and its co-occurrences does not matter (3) Unable to analyze how terms are dispersed in different documents. In the web service scenario, services are represented in short text form. TF-IDF does not work well in web service representation because of the reason that it is unable to effectively find the importance of a term concerning its occurrence in other documents. If we compare two service documents i.e. 's1' and 's2' first having a large and second having small number of terms respectively then TF-IDF does not demonstrate the importance of terms in 's1' as smaller to 's2'. Therefore, it is not possible to assign effective weights to the terms. In the lack of effective vector space representation, the performance of the clustering algorithm also degrades. In this paper, we propose a new approach i.e. LFW+K which is based on Length Feature Weight (LFW) for the vectorized representation of service followed by K-Means clustering. The proposed approach helps to find the informative term from web service and assigns the term weight accordingly by considering parameters like the dimension of the web service document, maximum frequency of a term in the document and occurrences of a term in other documents. LFW+K is applied on the datasets of real-world web services and the performance is measured using standard measurement criteria (i.e. precision, recall, F1-score, and accuracy). Results of the proposed approach are compared with K-Means clustering on TF-IDF representation method i.e. TF-IDF+K. Results show that the proposed method outperforms the clustering done by using TF-IDF method for vector space representation of web services.