Reviewerly/exHarmony huggingface dataset:
Self-controlling photonic-on-chip networks with deep reinforcement learning

We present a novel photonic chip design for high bandwidth four-degree optical switches that support high-dimensional switching mechanisms with low insertion loss and low crosstalk in a low power consumption level and a short switching time. Such four-degree photonic chips can be used to build an integrated full-grid Photonic-on-Chip Network (PCN). With four distinct input/output directions, the proposed photonic chips are superior compared to the current bidirectional photonic switches, where a conventionally sizable PCN can only be constructed as a linear chain of bidirectional chips. Our four-directional photonic chips are more flexible and scalable for the design of modern optical switches, enabling the construction of multi-dimensional photonic chip networks that are widely applied for intra-chip communication networks and photonic data centers. More noticeably, our photonic networks can be self-controlling with our proposed Multi-Sample Discovery model, a deep reinforcement learning model based on Proximal Policy Optimization. On a PCN, we can optimize many criteria such as transmission loss, power consumption, and routing time, while preserving performance and scaling up the network with dynamic changes. Experiments on simulated data demonstrate the effectiveness and scalability of the proposed architectural design and optimization algorithm. Perceivable insights make the constructed architecture become the self-controlling photonic-on-chip networks.
Spotlight on Phishing: A Longitudinal Study on Phishing Awareness Trainings

Phishing is in practice one of the most common attack vectors threatening digital assets. An attacker sends a legitimate-looking e-mail to a victim to lure her on a website with the goal of tricking the victim into revealing credentials. A phishing e-mail can use both technical (e.g., a forged link) and psychological vectors (e.g., an authoritarian tone) to persuade the victim. In this paper, we present an analysis of more than 420,000 phishing e-mails sent over more than 1.5 years by a consulting company offering awareness trainings. Our data set contains detailed information on how users interact with the e-mails, e.g., when they click on links and what psychological vectors are used in the e-mails to convince the recipient of its legitimacy. While previous studies often used lab environments, the e-mails in our data set are sent to real users during their day-to-day work so that we can study their behavior in a genuine setting. Our results indicate a continually decreasing click rate (from 19% to 10%) with progressing awareness training. We also found some psychological vectors, including an authoritative tone and curiosity, to be more effective than others to trick a user into falling for this type of scam e-mails.
Experimental demonstration of the conversion of local and correlated Gaussian quantum coherence

Quantum coherence plays an important role in quantum information processing. In this Letter, we experimentally demonstrate the conversion of local and correlated Gaussian quantum coherence in the process of converting two squeezed states into an entangled state. We also investigate the relationship among total, local, and correlated coherence and show that the total coherence of a two-mode Gaussian state is the sum of local quantum coherence of each mode and the correlated quantum coherence between two modes. Our results highlight the connection of different quantum coherence in a two-mode Gaussian system and provide references for potential application.
Adding Metalogic Features to Knowledge Representation Languages*

In this paper we present a methodology for introducing customizable metalogic features in logic-based knowledge representation and reasoning languages. The proposed approach is based on concepts of introspection and reflection previously introduced and discussed by various authors in relevant literature. This allows a knowledge engineer to specify enhanced reasoning engines by defining properties and meta-properties of relations as expressible for instance in OWL. We employ meta-level axiom schemata based upon a naming (reification) device. We propose general principles for extending the semantics of “host” formalisms accordingly. Consequently, suitable pre-defined libraries of properties can be made available, while user-defined new schemata are also allowed. We make the specific cases of Answer Set Programming (ASP) and Datalog±, where such features may be part of software engineering toolkits for these programming paradigms. On the one hand, concerning ASP, we extend the programming principles and practice to accommodate the proposed methodology, so as to perform meta-reasoning within the plain ASP semantics. The computational complexity of the resulting framework does not change. On the other hand, we show how metalogic features can significantly enrich Datalog± with minor changes to its operational semantics (provided in terms of “chase”) and, also in this case, no additional complexity burden.
General Models for Handwritten Text Recognition: Feasibility and State-of-the Art. German Kurrent as an Example

Existing text recognition engines enables to train general models to recognize not only one specific hand but a multitude of historical hands within a particular script, and from a rather large time period (more than 100 years). This paper compares different text recognition engines and their performance on a test set independent of the training and validation sets. We argue that both, test set and ground truth, should be made available by researchers as part of a shared task to allow for the comparison of engines. This will give insight into the range of possible options for institutions in need of recognition models. As a test set, we provide a data set consisting of 2,426 lines which have been randomly selected from meeting minutes of the Swiss Federal Council from 1848 to 1903. To our knowledge, neither the aforementioned text lines, which we take as ground truth, nor the multitude of different hands within this corpus have ever been used to train handwritten text recognition models. In addition, the data set used is perfect for making comparisons involving recognition engines and large training sets due to its variability and the time frame it spans. Consequently, this paper argues that both the tested engines, HTR+ and PyLaia, can handle large training sets. The resulting models have yielded very good results on a test set consisting of unknown but stylistically similar hands.
Qualitative Data Clustering to Detect Outliers

Detecting outliers is a widely studied problem in many disciplines, including statistics, data mining, and machine learning. All anomaly detection activities are aimed at identifying cases of unusual behavior compared to most observations. There are many methods to deal with this issue, which are applicable depending on the size of the data set, the way it is stored, and the type of attributes and their values. Most of them focus on traditional datasets with a large number of quantitative attributes. The multitude of solutions related to detecting outliers in quantitative sets, a large and still has a small number of research solutions is a problem detecting outliers in data containing only qualitative variables. This article was designed to compare three different categorical data clustering algorithms: K-modes algorithm taken from MacQueen’s K-means algorithm and the STIRR and ROCK algorithms. The comparison concerned the method of dividing the set into clusters and, in particular, the outliers detected by algorithms. During the research, the authors analyzed the clusters detected by the indicated algorithms, using several datasets that differ in terms of the number of objects and variables. They have conducted experiments on the parameters of the algorithms. The presented study made it possible to check whether the algorithms similarly detect outliers in the data and how much they depend on individual parameters and parameters of the set, such as the number of variables, tuples, and categories of a qualitative variable.
Compilation and Analysis of Requirements for the Design of an Explosive Ordnance Disposal Robot Prototype Applied in UDEX-Arequipa

Advances in the field of robotics will help in the work of the Explosive Ordnance Disposal Unit of Arequipa (UDEX-AQP) allowing to safeguard the lives of the Explosive Ordnance Disposal Technicians (TEDAX). This generates the need to understand the different UDEX procedures in detail in each intervention they perform in order to extract the minimum requirements for the design of the EOD robot that will be accepted by UDEX-AQP. In the paper we will analyze the different data collected for the development of the EOD robot prototype to be used in UDEX interventions. The information is obtained from the report of reported cases from the period 2013 to 2020 and the support of UDEX-AQP, to extract features and design an intuitive control as well as an optimal user experience. The study revealed the most important aspects for the design of the robot according to the needs of TEDAX such as shape and weight of explosives, frequency of explosives encountered; as well as the most challenging environments in the interventions. We conducted tests with the Allen Vanguard MK2 robot to innovate the human-robot experience, through surveys we collected aptitudes and skills with this technology by the TEDAX, with this data we can design an interface that generates a friendly user experience.
A Survey on the Relevance of the Performance of Model Transformations.

In Model-Driven Engineering (MDE) model transformation languages are used to describe important operations on models.Such domain-specific languages are specially developed to describe transformation rules, according to which an output model should be generated from an input model.In comparison to these domain-specific languages, techniques to analyze and improve the performance of programs written in a general-purpose language, such as Java or C, are well known.However, are such techniques also needed for model transformation languages?Problem.Since these languages are only used in certain domains; the first question is whether performance is at all relevant for model transformations and whether techniques similar to those used to analyze and improve the performance of general-purpose languages are needed.Research in the performance of model transformations focuses mainly on comparing the performance of different languages or different definition styles or optimizing the engine that executes the transformation.However, it is not clear to what extent these efforts can mitigate or prevent performance issues, and there is also a lack of studies that examine to what extend the performance of transformations is relevant.Method.In order to close this gap and to answer the initial question about the relevance of performance, we conducted an online survey.For this purpose, we developed a questionnaire and identified 649 authors as potential participants based on a Systematic Literature Review (SLR) on a selection of model transformation languages.Additionally, we were able to acquire four further potential participants by advertising our study.In total, 84 participants took part in our survey.We used statistical tests such as Kendall's τ c , the Kruskal-Wallis-Test and the Mann-Whitney-U-Test to evaluate our hypotheses on relevant factors for the performance of model transformations.Results.The results show that specific performance is desired and that there is a willingness to improve performance.In this regard, we identified a need for insights necessary to better understand how a transformation is performed in order to be able to improve its performance.Furthermore, we investigated with the help of hypotheses tests the possible influencing factors that cause participants to try to analyze or improve the performance of model transformations.The main results of the hypotheses tests are that the satisfaction with the execution time, the size of the models used, the relevance of whether a specific execution time is not exceeded in the average case, and the knowledge of how a transformation engine executes a transformation are relevant factors.
Property Inference Attacks on Convolutional Neural Networks: Influence and Implications of Target Model’s Complexity

Machine learning models' goal is to make correct predictions for specific tasks by learning important properties and patterns from data. By doing so, there is a chance that the model learns properties that are unrelated to its primary task. Property Inference Attacks exploit this and aim to infer from a given model (i.e., the target model) properties about the training dataset seemingly unrelated to the model's primary goal. If the training data is sensitive, such an attack could lead to privacy leakage. This paper investigates the influence of the target model's complexity on the accuracy of this type of attack, focusing on convolutional neural network classifiers. We perform attacks on models that are trained on facial images to predict whether someone's mouth is open. Our attacks' goal is to infer whether the training dataset is balanced gender-wise. Our findings reveal that the risk of a privacy breach is present independently of the target model's complexity: for all studied architectures, the attack's accuracy is clearly over the baseline. We discuss the implication of the property inference on personal data in the light of Data Protection Regulations and Guidelines.
Development and Verification of an Internet Game Literacy Scale

Education on internet games for parents and internet game literacy are needed to prevent problematic internet game playing in Korea. We created an 18-item Internet Game Literacy Scale (IGLS). It is a valuable tool for assessing the positive and negative aspects of internet game play. We aimed to determine the validity of the IGLS and the cut-off for the tendency for internet gameplay.An online research company gathered data from 300 participants. Factor analysis, including Cronbach's α and consistency coefficient, exploratory factor analysis, and confirmatory factor analysis were conducted to verify the 18 items of the IGLS. Additionally, a K-means cluster analysis was performed to determine the cut-off values for positive and negative IGLS scores.The 18 items of the IGLS were proven to be reliable, as evidenced by a high Cronbach's alpha (α = 0.892). The Kaiser-Meyer-Olkin measure of sampling adequacy was 0.903, and Bartlett's test of sphericity was good (χ² = 1,623.314, P < 0.001). All 18 items were segregated into two factors, with nine items each. The eigenvalue of all 18 items was significant at > 0.4. In the analysis of the validity of the 18-item IGLS with confirmatory factor analysis (CFA) (maximum likelihood estimation, with an oblique method), the fit indices of the standard three-factor model reached acceptable standards. The cut-off point of the total score between the low positive and average positive groups was 23, and the cut-off point of the total score between the average positive and high positive groups was 30. The cut-off point of the total score between the low negative and the average negative groups was 24. The cut-off point of the total score between the average negative group and the high negative group was 32.The study assessed the reliability and validity of the IGLS and suggested a cut-off for low, average, and high Internet game literacy degree with 300 Korean adults aged 21-49 years. The current results suggest that the IGLS has good internal consistency and a proper cut-off for positive and negative internet game literacy degrees.
IBM MNLP IE at CASE 2021 Task 2: NLI Reranking for Zero-Shot Text Classification

Supervised models can achieve very high accuracy for fine-grained text classification.In practice, however, training data may be abundant for some types but scarce or even nonexistent for others.We propose a hybrid architecture that uses as much labeled data as available for fine-tuning classification models, while also allowing for types with little (fewshot) or no (zero-shot) labeled data.In particular, we pair a supervised text classification model with a Natural Language Inference (NLI) reranking model.The NLI reranker uses a textual representation of target types that allows it to score the strength with which a type is implied by a text, without requiring training data for the types.Experiments show that the NLI model is very sensitive to the choice of textual representation, but can be effective for classifying unseen types.It can also improve classification accuracy for the known types of an already highly accurate supervised model. 1
Off-Network Earthquake Location by Earthquake Early Warning Systems: Methodology and Validation

ABSTRACT Regional source-based earthquake early warning systems perform three consecutive tasks: (1) detection and epicenter location, (2) magnitude determination, and (3) ground-motion prediction. The correctness of the magnitude determination is contingent on that of the epicenter location, and the credibility of the ground-motion prediction depends on those of the epicenter location and the magnitude determination. Thus, robust epicenter location scheme is key for regional earthquake early warning systems. Available source-based systems yield acceptably accurate locations when the earthquakes occur inside the real-time seismic network, but they return erroneous results otherwise. In this study, a real-time algorithm that is intended as a supplement to an existing regional earthquake early warning systems is introduced with the sole objective of ameliorating its off-network location capacity. The new algorithm combines measurements from three or more network stations that are analyzed jointly using an array methodology to give the P-wave slowness vector and S-phase arrival time. Prior to the S-phase picking, the nonarrival of the S phase is used for determining a minimum epicentral distance. This estimate is updated repeatedly with elapsed time until the S phase is picked. Thus, the system timeliness is not compromised by waiting for the S-phase arrival. After the S wave is picked, an epicentral location can be determined using a single array by intersecting the back-azimuth beam with the S-minus-P annulus. When several arrays are assembled, the back azimuth and P and S picks from all arrays are combined to constrain the epicenter. The performance of the array processing for back azimuth and S-wave picking is assessed using a large number of accelerograms, recorded by nine strong motion sensors of the KiK-net seismic network in Japan. The nine stations are treated as three distinct seismic arrays, comprising three stations each. Good agreement is found between array-based and catalog-reported parameters. Finally, the advantage of the new array methodology with respect to alternative schemes for back azimuth and distance is demonstrated.
Extending Upon a Transfer Learning Approach for Brain Tumour Segmentation

The incidence of gliomas has been on the rise and are the most common malignant brain tumours diagnosed upon medical appointments. A common approach to identify and diagnose brain tumours is to use Magnetic Resonance Imaging (MRI) to pinpoint tumour regions. However, manual segmentation of brain tumours is highly time-consuming and challenging due to the multimodal structure of MRI scans coupled with the task of delineating boundaries of different brain tissues. As such, there is a need for automated and accurate segmentation techniques in the medical domain to reduce both time and task complexity. Various Deep Learning techniques such as Convolutional Neural Networks (CNN) and Fully Connected Networks (FCN) have been introduced to address this challenge with promising segmentation results on various datasets. FCNs such as U-Net in recent literature achieve state-of-the-art performance on segmentation tasks and have been adapted to tackle various domains. In this paper, we propose an improved extension upon an existing transfer learning method on the Brain Tumour Segmentation (BraTS) 2020 dataset and achieved marginally better results compared to the original approach.
Optimal-Time Queries on BWT-Runs Compressed Indexes.

Indexing highly repetitive strings (i.e., strings with many repetitions) for fast queries has become a central research topic in string processing, because it has a wide variety of applications in bioinformatics and natural language processing. Although a substantial number of indexes for highly repetitive strings have been proposed thus far, developing compressed indexes that support various queries remains a challenge. The run-length Burrows-Wheeler transform (RLBWT) is a lossless data compression by a reversible permutation of an input string and run-length encoding, and it has received interest for indexing highly repetitive strings. LF and ϕ^{-1} are two key functions for building indexes on RLBWT, and the best previous result computes LF and ϕ^{-1} in O(log log n) time with O(r) words of space for the string length n and the number r of runs in RLBWT. In this paper, we improve LF and ϕ^{-1} so that they can be computed in a constant time with O(r) words of space. Subsequently, we present OptBWTR (optimal-time queries on BWT-runs compressed indexes), the first string index that supports various queries including locate, count, extract queries in optimal time and O(r) words of space.
The card guessing game: A generating function approach

Consider a card guessing game with complete feedback in which a deck of n cards ordered 1,…,n is riffle-shuffled once. With the goal to maximize the number of correct guesses, a player guesses cards from the top of the deck one at a time under the optimal strategy until no cards remain. We provide an expression for the expected number of correct guesses with arbitrary number of terms, an accuracy improvement over the results of Liu (2021). In addition, using generating functions, we give a unified framework for systematically calculating higher-order moments. Although the extension of the framework to k≥2 shuffles is not immediately straightforward, we are able to settle a long-standing McGrath's conjectured optimal strategy described in Bayer and Diaconis (1992) by showing that the optimal guessing strategy for k=1 riffle shuffle does not necessarily apply to k≥2 shuffles.
Structure-to-Text Generation with Self-Training, Acceptability Classifiers and Context-Conditioning for the GEM Shared Task

We explore the use of self-training and acceptability classifiers with pre-trained models for natural language generation in structure-totext settings using three GEM datasets (E2E, WebNLG-en, Schema-Guided Dialog).With the Schema-Guided Dialog dataset, we also experiment with including multiple turns of context in the input.We find that self-training with reconstruction matching along with acceptability classifier filtering can improve semantic correctness, though gains are limited in the full-data setting.With context-conditioning, we find that including multiple turns in the context encourages the model to align with the user's word and phrasing choices as well as to generate more self-consistent responses.In future versions of the GEM challenge, we encourage the inclusion of few-shot tracks to encourage research on data efficiency.