Reviewerly/exHarmony huggingface dataset:
Information Asset Classification and Labelling Model Using Fuzzy Approach for Effective Security Risk Assessment

Many information assets have been exposed to threats as a result of business owners not having access to an efficient model for classifying their information assets into appropriate security risk levels, required level of protection and priority. Manual and machine learning among several approaches have been employed in classifying information; these were not effective in considering users' preferences and are also not based on security standard. This study proposed a Fuzzy-based Information Asset Classification and Labeling framework based on ISO/IEC 27001 security standard. The framework comprises of six major phases: expert based information asset security assessment and classification (IAC), fuzzy based information assets security assessment and classification (FIACL), information asset labeling, information security risk assessment, and information asset handling and information asset storage. The implementation of the model will be done by developing an automated system with an enhanced classification using fuzzy logic and Delphi method for the security risk assessment. The comparative analysis revealed that FIACL was more efficient and accurate in classifying information assets for proper security risks assessment and control. This model will foster effective classification of information assets in any organization.
Topology-Hiding Computation on All Graphs

A distributed computation in which nodes are connected by a partial communication graph is called topology hiding if it does not reveal information about the graph beyond what is revealed by the output of the function. Previous results have shown that topology-hiding computation protocols exist for graphs of constant degree and logarithmic diameter in the number of nodes (Moran–Orlov–Richelson, TCC’15; Hirt et al., Crypto’16) as well as for other graph families, such as cycles, trees, and low circumference graphs (Akavia–Moran, Eurocrypt’17), but the feasibility question for general graphs was open. In this work, we positively resolve the above open problem: we prove that topology-hiding computation is feasible for all graphs under either the decisional Diffie–Hellman or quadratic residuosity assumption. Our techniques employ random or deterministic walks to generate paths covering the graph, upon which we apply the Akavia–Moran topology-hiding broadcast for chain graphs (paths). To prevent topology information revealed by the random walk, we design multiple graph-covering sequences that, together, are locally identical to receiving at each round a message from each neighbor and sending back a processed message from some neighbor (in a randomly permuted order).
Solving Mean-Payoff Games via Quasi Dominions

Abstract We propose a novel algorithm for the solution of mean-payoff games that merges together two seemingly unrelated concepts introduced in the context of parity games, small progress measures and quasi dominions . We show that the integration of the two notions can be highly beneficial and significantly speeds up convergence to the problem solution. Experiments show that the resulting algorithm performs orders of magnitude better than the asymptotically-best solution algorithm currently known, without sacrificing on the worst-case complexity.
HATE SPEECH LAWS: EXPRESSIVE POWER IS NOT THE ANSWER

ABSTRACT According to the influential “expressive” argument for hate speech laws, legal restrictions on hate speech are justified, in significant part, because they powerfully express opposition to hate speech. Yet the expressive argument faces a challenge: why couldn't we communicate opposition to hate speech via counterspeech, rather than bans? I argue that the expressive argument cannot address this challenge satisfactorily. Specifically, I examine three considerations that purport to explain bans’ expressive distinctiveness: considerations of strength; considerations of directness; and considerations of complicity. These considerations either fail to establish that bans are expressively superior to counterspeech, or presuppose that bans successfully deter hate speech. This result severely undercuts the expressive argument's appeal. First, contrary to what its proponents suggest, this argument fails to circumvent the protracted empirical controversies surrounding bans’ effectiveness as deterrents. Second, the expressive argument appears redundant, because bans are expressively distinctive only insofar as hate speech is already suppressed.
An Algorithm for Reducing the Number of Distinct Branching Conditions in a Decision Forest

Given a decision forest, we study a problem of reducing the number of its distinct branching conditions without changing each tree's structure while keeping classification performance. A decision forest with a smaller number of distinct branching conditions can not only have a smaller description length but also be implemented by hardware more efficiently. To force the modified decision forest to keep classification performance, we consider a condition that the decision paths at each branching node do not change for $$100\sigma $$ % of the given feature vectors passing through the node for a given $$0\le \sigma <1$$ . Under this condition, we propose an algorithm that minimizes the number of distinct branching conditions by sharing the same condition among multiple branching nodes. According to our experimental results using 13 datasets in UCI machine learning repository, our algorithm succeeded more than 90% reduction on the number of distinct branching conditions for random forests learned from 3 datasets without degrading classification performance. 90% condition reduction was also observed for 7 other datasets within 0.17 degradation of prediction accuracy from the original prediction accuracy at least 0.673.
Curiosity-Driven Variational Autoencoder for Deep Q Network

In recent years, deep reinforcement learning (DRL) has achieved tremendous success in high-dimensional and large-scale space control and sequential decision-making tasks. However, the current model-free DRL methods suffer from low sample efficiency, which is a bottleneck that limits their performance. To alleviate this problem, some researchers used the generative model for modeling the environment. But the generative model may become inaccurate or even collapse if the state has not been sufficiently explored. In this paper, we introduce a model called Curiosity-driven Variational Autoencoder (CVAE), which combines variational autoencoder and curiosity-driven exploration. During the training process, the CVAE model can improve sample efficiency while curiosity-driven exploration can make sufficient exploration in a complex environment. Then, a CVAE-based algorithm is proposed, namely DQN-CVAE, that scales CVAE to higher dimensional environments. Finally, the performance of our algorithm is evaluated through several Atari 2600 games, and the experimental results show that the DQN-CVAE achieves better performance in terms of average reward per episode on these games.
Human Posture Recognition Method Based On Skeleton Vector With Depth Sensor

Abstract This paper proposes a human body gesture recognition method. The method collects bone information of the human body through the Kinect depth sensor, and then uses the direction cosine method for feature extraction.Finally, the feature vector is sent to the BP neural network for training and recognition. In the case of less sample training, the system can still accurately identify the five postures of standing, sitting, leaning forward, leaning backward and underarm. The input is composed of low-dimensional bone information, so the speed and recognition response speed during network training is better than the conventional RGB image recognition method. Experiments show that the recognition rate and real-time performance of the system can meet the needs of practical applications.
Deterministic Sampling of Multivariate Densities based on Projected Cumulative Distributions

We approximate general multivariate probability density functions by deterministic sample sets. For optimal sampling, the closeness to the given continuous density has to be assessed. This is a difficult challenge in multivariate settings. Simple solutions are restricted to the one-dimensional case. In this paper, we propose to employ one-dimensional density projections. These are the Radon transforms of the densities. For every projection, we compute their cumulative distribution function. These Projected Cumulative Distributions (PCDs) are compared for all possible projections (or a discrete set thereof). This leads to a tractable distance measure in multivariate space. The proposed approximation method is efficient as calculating the distance measure mainly entails sorting in one dimension. It is also surprisingly simple to implement.
Single image reflection removal based on structure-texture layering

In computer vision, the reflections impair the overall perception of images and introduce undesired interference to high-level image understanding. To date, reflection removal approaches have been developed with advantage of visual quality enhancement. However, the reflection removal for single image is still a challenging task with its performance to be further boosted. During this process, additional visual artifacts such as blocking may also be introduced that degrades subjective quality of images. In this paper, we make the first attempt to eliminate image reflections and artifacts simultaneously within a unified framework. A structure-texture strategy is utilized to decompose the image into structure and texture layers. The proposed model handles reflection removal in both layers and further suppress blocking artifacts in the texture layer. Finally, a fusion process is employed to obtain the result image. Experiments with objective and subjective evaluations validate the superiority of the proposed method, which outperforms several state-of-the-art algorithms.
Lexicon-Based Indonesian Local Language Abusive Words Dictionary to Detect Hate Speech in Social Media

Background: Hate speech is an expression to someone or a group of people that contain feelings of hate and/or anger at people or groups. On social media users are free to express themselves by writing harsh words and share them with a group of people so that it triggers separations and conflicts between groups. Currently, research has been conducted by several experts to detect hate speech in social media namely machine learning-based and lexicon-based, but the machine learning approach has a weakness namely the manual labelling process by an annotator in separating positive, negative or neutral opinions takes time long and tiringObjective: This study aims to produce a dictionary containing abusive words from local languages in Indonesia. Lexicon-base is very dependent on the language contained in dictionary words. Indonesia has thousands of tribes with 2500 local languages, and 80% of the population of Indonesia use local languages in communication, with the result that a significant challenge to detect hate speech of social media.Methods: Abusive words surveys are conducted by using proportionate stratified random sampling techniques in 4 major tribes on the island of Java, namely Betawi, Sundanese, Javanese, MadureseResults: The experimental results produce 250 abusive words dictionary from 4 major Indonesian tribes to detect hate speech in Indonesian social media by using the lexicon-based approach. Conclusion: A stratified random sampling technique has been conducted in 4 major Indonesian tribes to produce 250 abusive words for hate speech detection using the lexicon-based approach.
Generalization of the ball-collision algorithm

In this paper we generalize the ball-collision algorithm by&#x0D; Bernstein, Lange, Peters from the binary field to a general finite&#x0D; field. We also provide a complexity analysis and compare the&#x0D; asymptotic complexity to other generalized information set decoding&#x0D; algorithms.
FPGA-based object detection processor with HOG feature and SVM classifier

Computer vision is an important sensing technique to translate the information to decisions. In robotic applications, object detection is a critical skill to perform tasks for robots in complex environments. The deep-learning framework, e.g. You Only Look Once (YOLO), attracts much more attention recently. Moreover, it is not an optimal solution for a mobile robot since it requires a large scale of hardware resources, on-chip SRAMs, and power consumption. In this work, we report an object detection processor synchronizing the image sensor in FPGA with a cellbased histogram of oriented gradient (HOG) feature descriptor and support vector machine (SVM) classifier by parallel sliding window mechanism. The HOG feature extraction circuitry with pixel-based pipelined architecture constructs the cell-based feature vectors for parallelizing partial SVM-based classification in multiple sliding windows. The SVM classification produces the final result after accumulating the vector components in one sliding window. This framework can be used to both localize and recognize multiple objects in video footage. The proposed object processor, in which the SVM classifier is trained by INRIA datasets, is implemented and verified on Intel Stratix IV FPGA for the pedestrian.
Efficient Hamiltonian programming in qubit arrays with nearest-neighbor couplings

We consider the problem of selectively controlling couplings in a practical quantum processor with always-on interactions that are diagonal in the computational basis, using sequences of local NOT gates. This methodology is well-known in NMR implementations, but previous approaches do not scale efficiently for the general fully-connected Hamiltonian, where the complexity of finding time-optimal solutions makes them only practical up to a few tens of qubits. Given the rapid growth in the number of qubits in cutting-edge quantum processors, it is of interest to investigate the applicability of this control scheme to much larger scale systems with realistic restrictions on connectivity. Here we present an efficient scheme to find near time-optimal solutions that can be applied to engineered qubit arrays with local connectivity for any number of qubits, indicating the potential for practical quantum computing in such systems.
ℓ<sub>2</sub> − ℓ<sub>1</sub> Fidelity based Elastic Net Regularisation for Magnetic Resonance Image Denoising

In this paper, we design an optimization problem for denoising 3 dimensional Magnetic Resonance Image (MRI) data. The problem is formulated as a variational technique considering the underlying distribution of noise to be Gaussian and Laplacian. We propose a mixed noise removal technique which simultaneously handles noise of two different nature rather than initially detecting impulse noise corrupted outlier pixels and then handling additive white Gaussian noise. This is made possible by the combination of ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> - ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> data fidelity terms along with regularization penalty terms. We use elastic net regularization (combination of Total Variation (TV) and Tikhonov-Miller (TM) as two different regularization terms) to obtain better reconstruction quality. Experiments conducted on synthetically corrupted phantom brain MR data and real noisy knee MRI image obtained from patients show that our proposed technique outperforms comparing techniques even under higher intensity of noise levels.
Soundness Conditions for Big-Step Semantics

Abstract We propose a general proof technique to show that a predicate is sound , that is, prevents stuck computation, with respect to a big-step semantics . This result may look surprising, since in big-step semantics there is no difference between non-terminating and stuck computations, hence soundness cannot even be expressed . The key idea is to define constructions yielding an extended version of a given arbitrary big-step semantics, where the difference is made explicit. The extended semantics are exploited in the meta-theory, notably they are necessary to show that the proof technique works. However, they remain transparent when using the proof technique, since it consists in checking three conditions on the original rules only, as we illustrate by several examples.
Threshold identity authentication signature: Impersonation prevention in social network services

Summary While the social network services (SNS) have dominate the ways that people communicate with each other on the Internet, identity impersonation remains to be a serious issue that needs to be solved due to the anonymity in the cyber network. Currently, the potential solution to the problem relies heavily on the administration from the central server, which requires intensive workload of the identity management. In this article, we propose a threshold identity authentication signature scheme to solve the impersonation problem from the protocol layer rather than software design in the traditional upper level. In our scheme, with the help of some authenticated accounts, trusted relationship can be shared in a group to other unauthenticated accounts, which largely decrease the workload of authenticating all the accounts. Users are given the ability to verify other accounts' identity information by their signatures. Then, we establish three security goals to prevent the malicious adversary to launch the impersonation attack on a group. We claim that our scheme is suitable for the SNS scenario since the procedure of generating a signature to prove the identity requires little computation cost, it is user‐friendly especially on the lightweight devices such as mobile devices and so on.
Blockchain and Smart Contract for IoT

Integrating IoT with blockchains and Smart Contracts could be a solution of many problems in terms of security and scalability. A lot of applications are now implemented based on IoT Blockchain in smart governments and cities, industrial fields and smart transactions of sensitive data in order to maintain confidentiality and integrity of the data without the need of trusted third parties. However, we explained the terms Internet of things, Blockchains, Smart Contracts and how they work in separate. also suggested an architectural guideline for blockchain enabled IoT devices which facilitates sharing them between multiple blockchain ecosystems and at the same time, ensures the exclusive access to them seamlessly through blockchain smart contracts.
Financial product recommendation system based on transformer

With the growth of the variety and quantity of Internet financial products, how to effectively implement personalized recommendation has become the key issue. The particularity of Internet financial products lies in that attribute value (such as 7-day annualized income) is not fixed. Users will consider the attribute value of the product at that time when choosing the product at different times. The importance of timing factor in financial product recommendation cannot be ignored. However, the traditional financial product recommendation algorithm is based on the static attributes of users and financial products, ignoring the factors of time in series data, and the recommendation quality is low. With the development of artificial intelligence, deep learning technology has been widely used in personalized recommendation system. Therefore, taking advantage of the advantages of transformer in processing time series, an R-Transformer(Recommendation system based on transformer) network has proposed. Two R-Transformer networks are used to mine users' and financial products' states based on time series, and the inner product of users' and financial products' states is taken as the final score. Experimental results show that compared with the traditional collaborative filtering and RNN algorithm, this algorithm can effectively reduce RMSE and improve the F-measure of prediction.
Gradient Boosted Decision Tree based Classification for Recognizing Human Behavior

Human behavior prediction became an active research topic to determine the criminal and suspicious activities of a person. Gerontology deals with the everyday life activities of an individual including walking, climbing, eating, drinking, sitting and so on. It helps in ambient assisted living for the old persons in a self-reliant manner. The emergence of sensors and smart environment made the sensing process in an easier way. In general, the sensed dare classified using decision tree logic-based approach. The classification accuracy is low in case of decision tree approach. Hence, in this paper the gradient boosted tree is integrated with the decision tree approach to achieve greater accuracy. The triaccelerometer wearable sensor is used to collect the three-dimensional data of each activity of human being. The results showed that the integrated approach showed better accuracy and less error rate.
Continuous authentication with a focus on explainability

Traditional explicit authentication mechanisms, in which the device remains unlocked after the introduction of some kind of password, are slowly being complemented with the so-called implicit or continuous authentication mechanisms. In the latter, the user is constantly monitored in one or more ways, in search for signs of unauthorized access, which may happen if a third party has access to the phone after it has been unlocked. There are some different forms of continuous authentication, some of which based on Machine Learning. These are generally black box models, that provide a decision but not an explanation. In this paper we propose an approach for continuous authentication based on behavioral biometrics, machine learning, and that includes domain-dependent aspects for the user to interpret the actions and decisions of the system. It is non-intrusive, does not require any additional hardware, and can be used continuously to monitor user identity.
Evolution of Localization Length during Postpeak Response of Steel in Tension: Experimental Study

This paper presents an experimental study into the influence of localization length (Lc) and its evolution during the postpeak response of cylindrical low-carbon steel bars. A novel longitudinal strain method for the determination of the localization length is compared to the average strain method. Four specimen lengths [38.1 mm (1.5 in.), 76.2 mm (3 in.), 152.4 mm (6 in.), and 304.8 mm (12 in.)] with a diameter of 12.7 mm (0.5 in.) were tested according to ASTM A370 standards. A three-dimensional digital image correlation (3D DIC) measurement system was used to record the longitudinal and transverse deformations along the specimen length. The results indicate that the engineering stress-strain relation up to the peak load is almost identical for all tested specimens. However, the postpeak response shows a steeper softening response for longer specimens. Two methods were used to extract the localization length from the longitudinal engineering strain profile. The average strain method shows a length dependence for different specimen lengths, while the longitudinal strain method eliminates the length dependence. The consistency of the localization length obtained through the longitudinal strain method enables improved generalized analytical relationships. Linear and exponential curve fitting formulae are given to predict the localization length for a generic specimen.