Reviewerly/exHarmony huggingface dataset:
Information Security Behavioural Threshold Analysis in Practice: An Implementation Framework

This paper presents the development of a framework for evaluating group behaviour in information security in practice. Information security behavioural threshold analysis is employed as the theoretical foundation for the proposed framework. The suitability of the proposed framework is evaluated based on two sets of qualitative measures (general frameworks and information security frameworks) which were identified from literature. A novel conceptual mapping of the two sets of evaluation measures is presented and used to evaluate the proposed framework. The successful evaluation of the proposed framework, guided by the identified evaluation measures, is presented in terms of positive practical applications, as well as positive peer review and publication of the underlying theory.
Observation of photon antibunching with only one standard single-photon detector

The second-order photon correlation function g2(τ) is of great importance in quantum optics. g2(τ) is typically measured with the Hanbury Brown and Twiss (HBT) interferometer, which employs a pair of single-photon detectors and a dual-channel time acquisition module. Here, we demonstrate a new method to measure and extract g2(τ) with a standard single-photon avalanche photodiode (dead-time = 22 ns) and a single-channel time acquisition module. This is realized by shifting the coincidence counts of interest to a time window not affected by the dead-time and after-pulse of the detection system using a fiber-based delay line. The new scheme is verified by measuring g2(τ) from a single colloidal nanocrystal. Photon antibunching is unambiguously observed and agrees well with the result measured using the standard HBT setup. Our scheme simplifies the higher-order correlation technique and might be favored in cost-sensitive circumstances.
Quality In, Quality Out: Learning from Actual Mistakes

Approaches to Quality Estimation (QE) of machine translation have shown promising results at predicting quality scores for translated sentences. However, QE models are often trained on noisy approximations of quality annotations derived from the proportion of post-edited words in translated sentences instead of direct human annotations of translation errors. The latter is a more reliable ground-truth but more expensive to obtain. In this paper, we present the first attempt to model the task of predicting the proportion of actual translation errors in a sentence while minimising the need for direct human annotation. For that purpose, we use transfer-learning to leverage large scale noisy annotations and small sets of high-fidelity human annotated translation errors to train QE models. Experiments on four language pairs and translations obtained by statistical and neural models show consistent gains over strong baselines.
Coordinated Data Flow Control in IoT Networks

An IoT cloud environment consists of connected physical devices communicating with the cloud, sending telemetry data and accepting actuation information. For sensors, the data flow is from the physical devices to the cloud. The IoT edge device is responsible for collecting this data and forwarding it to the cloud environment for processing. The time it takes for the data to be made available for processing in the cloud is critical, and the network connectivity, bandwidth and latency are the bottlenecks. In this work, we created a flow controller which adaptively controls the flow of the data from the edge device to the cloud. While rate limiting is a trivial technique to control data flow, it is crucial how the edge devices dynamically control the data rate by re-configuring the IoT devices to send data based on the current network condition and load on the Edge device. We tested this system with a simulated data flow from 10 sensors to a Raspberry Pi Device which performed the rate limiting.
Using Reinforcement Learning to Optimize Quantum Circuits in the Presence of Noise

As we move towards devices which utilize more qubits, it becomes increasing more important to map quantum circuits in a way that uses resources efficiently as well as maximizes the reliability of the results of that circuit. To this end, we will need to rely on heuristic algorithms, specifically reinforcement learning (RL) as a method of building quantum circuits based on observations of the noise characteristics in its environment.
Study on Representation Invariances of CNNs and Human Visual Information Processing Based on Data Augmentation

Representation invariance plays a significant role in the performance of deep convolutional neural networks (CNNs) and human visual information processing in various complicated image-based tasks. However, there has been abounding confusion concerning the representation invariance mechanisms of the two sophisticated systems. To investigate their relationship under common conditions, we proposed a representation invariance analysis approach based on data augmentation technology. Firstly, the original image library was expanded by data augmentation. The representation invariances of CNNs and the ventral visual stream were then studied by comparing the similarities of the corresponding layer features of CNNs and the prediction performance of visual encoding models based on functional magnetic resonance imaging (fMRI) before and after data augmentation. Our experimental results suggest that the architecture of CNNs, combinations of convolutional and fully-connected layers, developed representation invariance of CNNs. Remarkably, we found representation invariance belongs to all successive stages of the ventral visual stream. Hence, the internal correlation between CNNs and the human visual system in representation invariance was revealed. Our study promotes the advancement of invariant representation of computer vision and deeper comprehension of the representation invariance mechanism of human visual information processing.
Survival Analysis and Prediction Model of IP Address Assignment Duration

IP addresses of end hosts change when they are re-assigned. We apply survival analysis, which is commonly used in healthcare, on IP addresses to predict their assignment duration (their lifetime). We propose a survival parametric model based on a history of 6 years of address assignments on a worldwide scale. Our model outperforms alternative models both from short-term and long-term views. The custom modelling is also discussed as address assignment varies across Internet service providers (ISPs) and autonomous systems (ASs). A predictable address assignment duration has many applications, including source reputation, topology mapping, and geolocation. We describe a use-case in fraud prevention, where the proposed model is used as a trigger for two-factor authentication. The created dataset of addresses assignment durations is made publicly available.
AN EXAMINATION OF THE CHALLENGES POSED BY CRYPTOCURRENCIES TO AML/CTF REGULATION

Amsterdam Law Forum (ALF) is the student-run 'International Law Journal' of VU University. Every year ALF publishes a winter, spring, and summer issue. The journal consists of three sections; scientific articles, opinion articles, and commentaries. As of this year, ALF also creates a section for inaugural speeches. In addition, ALF hosts a conference in spring with a relevant legal theme, where renowned speakers are invited to share their perspectives. Overall, ALF is a topical journal that provides a platform for established scholars and young academics to share knowledge, opinions and experiences and to make contributions to the international law discourse. Staff, PhD students and master students who have written a very good thesis are invited to submit an article to ALF. What is learned in the cradle is carried to the tomb: we are looking forward to sharing your articles on our website!
Materialized View Selection Using Discrete Quantum Based Differential Evolution Algorithm

A Data warehouse is a structure that stores big amount of data. This data is exploited in the best possible ways in order to improve the efficiency of decision-making. The huge volume of data makes answering queries complex and time-consuming. Therefore, materialized views are used in order to reduce the query processing time. Since materializing all views is not possible, due to space and maintenance constraints, materialized view selection became one of the crucial decisions in designing a data warehouse for optimal efficiency. In this paper, we propose a discrete - quantum based - version of Differential Evolution DE algorithm named QDE algorithm to solve the materialized view selection (MVS) problem with space constraint. This algorithm is a merging of the original DE with Quantum Evolutionary (QEA) algorithm. The experimental results show the efficiency of the proposed algorithm compared to well known algorithms used to solve MVS problem with space constraint such as HRUA and GEA.
AI Denoising to Accelerate Detector Simulation

Detector simulation is critical to experimental HEP; however this simulation (commonly done through toolkits such as Geant4) is computationally intensive. Performance can be improved somewhat through technical optimization, but more is needed. Using machine learning (ML) to accelerate simulation is a promising field, however efforts to use generative adversarial networks (GANs) or optimized autoencoders have faced issues. Using convolutional neural networks (CNNs) for denoising has been successful in non-HEP applications such as image processing. This poster investigates the efficacy of using CNNs to denoise Geant4 simulations. This could increase the accuracy of simulations performed under settings designed to increase computational efficiency.
Higher‐Order Nonclassicality in Photon Added and Subtracted Qudit States

Higher-order nonclassical properties of r photon added and t photon subtracted qudit states (referred to as rPAQS and tPSQS, respectively) are investigated here to answer: How addition and subtraction of photon can be used engineer higher-order nonclassical properties of qudit states? To obtain the answer higher-order moment of relevant bosonic field operators are first obtained for rPAQS and tPSQS, and subsequently the same is used to study the higher-order nonclassical properties of the corresponding states. A few witnesses of higher-order nonclassicality (e.g. Higher-Order Antibunching, Higher-Order Squeezing of Hillery type, Higher-Order sub-Poissonian Photon statistics) are first used to establish that rPAQS and tPSQS are highly nonclassical. These witnesses are found to indicate that the amount of nonclassicality enhances with the number of photon added (r). To quantitatively establish this observation and to make a comparison between rPAQS and tPSQS, volumes of the negative part of Wigner function (nonclassical volume) of rPAQS and tPSQS are computed as a quantitative measure of nonclassicality. Finally, for the sake of verifiablity of the obtained results, optical tomograms are also reported which can be obtained experimentally and used to produce Wigner function (which is not directly measurable in general) by the Radon transform. Throughout the study, a particular type of qudit state named as a new generalized binomial state is used as an example.
Autonomous learning of features for control: Experiments with embodied and situated agents

As discussed in previous studies, the efficacy of evolutionary or reinforcement learning algorithms for continuous control optimization can be enhanced by including a neural module dedicated to feature extraction trained through self-supervised methods. In this paper we report additional experiments supporting this hypothesis and we demonstrate how the advantage provided by feature extraction is not limited to problems that benefit from dimensionality reduction or that involve agents operating on the basis of allocentric perception. We introduce a method that permits to continue the training of the feature-extraction module during the training of the policy network and that increases the efficacy of feature extraction. Finally, we compare alternative feature-extracting methods and we show that sequence-to-sequence learning yields better results than the methods considered in previous studies.
Identity-Based Authenticated Encryption with Identity Confidentiality

Identity-based cryptography (IBC) is fundamental to security and privacy protection. Identity-based authenticated encryption (i.e., signcryption) is an important IBC primitive, which has numerous and promising applications. After two decades of research on signcryption, recently a new cryptographic primitive, named higncryption, was proposed. Higncryption can be viewed as privacy-enhanced signcryption, which integrates public key encryption, entity authentication, and identity concealment (which is not achieved in signcryption) into a monolithic primitive. Here, briefly speaking, identity concealment means that the transcript of protocol runs should not leak participants’ identity information. In this work, we propose the first identity-based higncryption ( $$\mathsf {IBHigncryption}$$ ). The most impressive feature of $$\mathsf {IBHigncryption}$$ , among others, is its simplicity and efficiency. The proposed $$\mathsf {IBHigncryption}$$ scheme is essentially as efficient as the fundamental CCA-secure Boneh-Franklin IBE scheme [12], while offering entity authentication and identity concealment simultaneously. Compared to the identity-based signcryption scheme [8], which is adopted in the IEEE P1363.3 standard, our $$\mathsf {IBHigncryption}$$ scheme is much simpler, and has significant efficiency advantage in total. Besides, our $$\mathsf {IBHigncryption}$$ enjoys forward $$\mathsf {ID}$$ -privacy, receiver deniability and x-security simultaneously. In addition, the proposed $$\mathsf {IBHigncryption}$$ has a much simpler setup stage with smaller public parameters, which in particular does not have the traditional master public key.
Public Tendering Processes Based on Blockchain Technologies

The lack of transparency is one of the problems affecting public tendering processes. In this paper, we will study some of the solutions based on blockchain technology and smart contracts on this topic. The conclusions are that the application of blockchain technology to public bidding processes is feasible, although it is also necessary to take into account the legislation of each country to which it will be applied.
String Diagrams for Regular Logic (Extended Abstract)

Regular logic can be regarded as the internal language of regular categories, but the logic itself is generally not given a categorical treatment. In this paper, we understand the syntax and proof rules of regular logic in terms of the free regular category FRg(T) on a set T. From this point of view, regular theories are certain monoidal 2-functors from a suitable 2-category of contexts -- the 2-category of relations in FRg(T) -- to that of posets. Such functors assign to each context the set of formulas in that context, ordered by entailment. We refer to such a 2-functor as a regular calculus because it naturally gives rise to a graphical string diagram calculus in the spirit of Joyal and Street. We shall show that every natural category has an associated regular calculus, and conversely from every regular calculus one can construct a regular category.
A Genetic Attack Against Machine Learning Classifiers to Steal Biometric Actigraphy Profiles from Health Related Sensor Data

In this work, we propose the use of a genetic-algorithm-based attack against machine learning classifiers with the aim of 'stealing' users' biometric actigraphy profiles from health related sensor data. The target classification model uses daily actigraphy patterns for user identification. The biometric profiles are modeled as what we call impersonator examples which are generated based solely on the predictions' confidence score by repeatedly querying the target classifier. We conducted experiments in a black-box setting on a public dataset that contains actigraphy profiles from 55 individuals. The data consists of daily motion patterns recorded with an actigraphy device. These patterns can be used as biometric profiles to identify each individual. Our attack was able to generate examples capable of impersonating a target user with a success rate of 94.5%. Furthermore, we found that the impersonator examples have high transferability to other classifiers trained with the same training set. We also show that the generated biometric profiles have a close resemblance to the ground truth profiles which can lead to sensitive data exposure, like revealing the time of the day an individual wakes-up and goes to bed.
Bayesian nonparametric multivariate spatial mixture mixed effects models with application to American Community Survey special tabulations

Leveraging multivariate spatial dependence to improve the precision of estimates using American Community Survey data and other sample survey data has been a topic of recent interest among data users and federal statistical agencies. One strategy is to use a multivariate spatial mixed effects model with a Gaussian observation model and latent Gaussian process model. In practice, this works well for a wide range of tabulations. Nevertheless, in situations in which the data exhibit heterogeneity within or across geographies, and/or there is sparsity in the data, the Gaussian assumptions may be problematic and lead to underperformance. To remedy these situations, we propose a multivariate hierarchical Bayesian nonparametric mixed effects spatial mixture model to increase model flexibility. The number of clusters is chosen automatically in a data-driven manner. The effectiveness of our approach is demonstrated through a simulation study and motivating application of special tabulations for American Community Survey data.
Research status and future of quantum information technology

Quantum information technology is an emerging interdisciplinary field of quantum mechanics and information science, whose birth will promote a new era of human intelligence from classical technology to quantum technology. This paper elaborates on the research status and future of the field of quantum information technology. Moreover, this paper shows the vision of quantum technology, i.e., the construction of various types of quantum networks, including quantum cloud computing networks, distributed quantum computing, quantum sensor networks, and quantum key distribution networks. Quantum computers have transformed research from the laboratory to the development of practical devices led by these enterprises. Currently, research and development of quantum computers have shifted to the stage of noisy intermediate-scale quantum computers (NISQ). In the era of quantum technology, there is no absolutely secure secrecy system or omnipotent way of deciphering Information and information security has entered a new stage of “quantum offense and defense.
A Graph-Based Method for Predicting the Helpfulness of Product Opinions

This paper presents a new approach to predict the helpfulness of opinions. Usually, researchers in this area use tables of attribute-value to aggregate the features that represent the evaluated texts. Although that representation is common, it considers that the objects are independent. We argue that among the discriminant factors of the helpfulness of opinions, there are dependent factors of the relationship among the opinion-forming elements. Thus, we modeled this task as a network, considering the information of relations among objects in the network (comments, stars, and words). A regularization technique of graphs is used to extract the relevant features of graph structure and, after that, the comments are classified as helpful or unhelpful. We compared our network model with two baselines methods, one based on fuzzy logic and another based on Neural Networks. Our model outperformed the fuzzy logic and Neutal Network methods in 0.17 and 0.19 of F-measure, respectively. The main advantages of our approach are that few data are necessary to helpfulness classification and the relationships may help in the understanding the classification, explaining the reasons for a determinate classification.
Automatic semantic role labeling in Ancient Greek using distributional semantic modeling

This paper describes a first attempt to automatic semantic role labeling in Ancient Greek, using a supervised machine learning approach. A Random Forest classifier is trained on a small semantically annotated corpus of Ancient Greek, annotated with a large amount of linguistic features, including form of the construction, morphology, part-of-speech, lemmas, animacy, syntax and distributional vectors of Greek words. These vectors turned out to be more important in the model than any other features, likely because they are well suited to handle a low amount of training examples. Overall labeling accuracy was 0.757, with large differences with respect to the specific role that was labeled and with respect to text genre. Some ways to further improve these results include expanding the amount of training examples, improving the quality of the distributional vectors and increasing the consistency of the syntactic annotation.
Determination of the copper-nickel ores formation sequence of the Kun-Manye deposit (Amur region)

The purpose of the article is to determine the sequence of mineral formation of copper-nickel ores of Kun-Manie deposit, which is located in Zeya district of Amur region. Three ore chutes take part in the structure of the deposit. Ore-bearing formations are sheet and sheetlike bodies of ultra-basic composition of the Kun-Manien complex, lying among rocks of crystal foundation of the Early Archean. Among the rocks, hornblende differences of gabbro-pyroxenites and pyroxenites predominate. In addition to nickel, the ores contain a wide range of associated components. The ores oxidation zone within the deposit and the entire ore field is not developed. The relevance of the work is due to the fact that detailed studies of ore minerals have not previously been carried out. The study presented in the work was conducted by polarizing ore microscope on polished ore samples characterizing different zones of the ore body. The result of the study was the establishment of mineral paragenesis and the sequence of mineral formation. It has been determined that the main ore minerals are pyrrhotine, pentlandite, also found - pyrite, chalcopyrite, less often - ilmenite, magnetite, sphalerite, platinum group elements. Ore mineralization formed in two stages. The magmatic stage is an early and main mineral formation phases including pyrite-magnetite, polymetallic and pentlandite associations. The hydrothermal stage is a late phase involving a pyrite association.