Reviewerly/exHarmony huggingface dataset:
Exploring the Evolution of TLS Certificates

A vast majority of popular communication protocols such as HTTPS for the Internet employs the use of TLS (Transport Layer Security) to secure communication. As a result, there have been numerous efforts to improve the TLS certificate ecosystem such as Certificate Transparency logs and Free Automated CAs like LetsEncrypt. Our work highlights the effectiveness of these efforts using the Certificate Transparency logs as well as certificates collected via full IPv4 scans by validating them. We show that a large proportion of invalid certificates still exists and outline reasons why these certificates still exist. Additionally, we report unresolved security issues such as key sharing. Moreover, we show that the incorrect use of template certificates has led to incorrect SCTs being embedded in the certificates. Taken together, our results emphasize the continued involvement of the research community to improve the web's PKI ecosystem.
Quantifying User Password Exposure to Third-Party CDNs

Web services commonly employ Content Distribution Networks (CDNs) for performance and security. As web traffic is becoming 100% HTTPS, more and more websites allow CDNs to terminate their HTTPS connections. This practice may expose a website’s user sensitive information such as a user’s login password to a third-party CDN. In this paper, we measure and quantify the extent of user password exposure to third-party CDNs. We find that among Alexa top 50K websites, at least 12,451 of them use CDNs and contain user login entrances. Among those websites, 33% of them expose users’ passwords to the CDNs, and a popular CDN may observe passwords from more than 40% of its customers. This result suggests that if a CDN infrastructure has a vulnerability or an insider attack, many users’ accounts will be at risk. If we assume the attacker is a passive eavesdropper, a website can avoid this vulnerability by encrypting users’ passwords in HTTPS connections. Our measurement shows that less than 17% of the websites adopt this countermeasure.
Serious Game for the Screening of Central Auditory Processing Disorder in School-Age Children: Development and Validation Study

Currently, many central auditory processing disorder screening tests are available for children, and serious games (SGs) are frequently used as a tool for the diagnosis of different neural deficits and disorders in health care. However, it has not been possible to find a proposal that unifies both ideas. In addition, the validation and improvement of SGs, in general, does not take into account the player-game interaction, thus omitting valuable information about the playability and usability of the game.This study presented Amalia's Planet, a game conceived for use in school environments, which allows a first assessment of a child through their performance of the proposed tasks related to different aspects of auditory performance. In addition, the game defines a series of events in relation to the execution of the tasks, which were evaluated for the subsequent optimization of its performance and the improvement of its usability.Using screening tools based on the use of SG technologies, a total of 87 school-age children were evaluated to test the various hypotheses proposed in this study. By grouping users according to whether they had personal history of hearing pathologies, the discriminant power, playability, and usability of the final solution were examined using traditional statistical techniques and process mining (PM) algorithms.With a confidence level of 80% for test 2 (P=.19), there was no statistical evidence to reject the null hypothesis that a player's performance is affected by whether the player had a previous auditory pathology. Furthermore, the tool allowed the screening of 2 players initially categorized as healthy because of their low level of performance in the tests and the similarity of their behavior with that of the group of children with a previous pathology. With regard to the validation of the proposed solution, the use of PM techniques made it possible to detect the existence of events that lasted too long, which can lead to player frustration, and to discover small structural flaws in the game.SGs seem to be an appropriate tool for the screening of children at risk of central auditory processing disorder. Moreover, the set of PM techniques provides a reliable source of information regarding the playability and usability of the solution to the development team, allowing its continuous optimization.
CRAPPY goes embedded: Including low-cost hardware in experimental setups

In a previous publication, we presented CRAPPY, a Python module designed for researchers wishing to easily develop code for driving complex experimental setups. In a complemented version, now targeting a broader audience, we extended the framework to include low-cost sensors, actuators, and cameras. Using such devices, it is now possible to set up tests with CRAPPY in a more cost-effective and flexible way. This can be achieved from embedded computers like the Raspberry Pi, on which the module is now fully functional. Alternatively, we introduce a solution for driving embedded sensors and actuators directly from a regular PC. Other enhancements for the integration of microcontrollers and remote equipment are also described. Based on examples of existing tests driven using CRAPPY, we demonstrate in this paper the feasibility of including low-cost equipment in experimental setups to obtain relevant results.
DRFN: A unified framework for complex document layout analysis

Document layout analysis (DLA) plays a vital role in information processing and management. At this stage, the processing of non-Manhattan layout documents has become the bottleneck in implementing the universal document layout analysis framework. To address this challenge, we propose a Complex Document Semantic Structure Extraction non-Manhattan document layout dataset (CDSSE). Furthermore, we design a Dynamic Residual Feature fusion Network (DRFN) to integrate the feature differences between non-Manhattan layouts and Manhattan layouts. During the fusion process, the DRFN makes full use of low-dimensional information and maintains the integrity of high-level semantic information through a Dynamic Residual Fusion Block (DRF). To overcome model overfitting caused by data scarcity, we propose a novel Dynamic Selection Mechanism (DSM). We prove that the DRFN can achieve comparable results on all benchmark datasets. For the Manhattan layout document, F1 reached 89.5% on DSSE-200 and 95.1% on CS-150. For the non-Manhattan layout document, F1 reached 86.8% on CDSSE. In addition, we verified the effectiveness of the model structure. On all datasets, the performance of the model using DRF was significantly improved (DSSE-200: 76.6% vs. 80.3%, CS-150: 91.7% vs. 93.1%, 62.6% vs. 71.8%). The use of the DSM was also significantly improved (DSSE-200: 89.0% vs. 89.5%, CS-150: 94.3% vs. 95.1%, 84.8% vs. 86.8%).
PEDAGOGICAL CREATIVITY OF EDUCATORS UNDER THE CONDITIONS OF INNOVATIVE DEVELOPMENT OF MODERN EDUCATION

A feature of modern education is innovativeness - the ability to update.&#x0D; Therefore, improving the quality of modern education is impossible without the creation of an innovative space, that is, the study and active implementation of innovative pedagogical technologies in practice, the main task of which is to ensure a new quality of education, which involves improving learning, education and personality development. Innovative in terms of content and nature, the dynamics of world trends require the modern teacher to mobilize creative potential in order to provide quality educational services and train a highly qualified competitive specialist in the modern labour market. In order to achieve effective transfer of knowledge and formation of competences, the teacher must show creative initiative when choosing modern forms and methods of work of innovative orientation, using certain criteria that allow judging the effectiveness of this or that innovation, creating an appropriate atmosphere of creative, searching, innovative activity. Therefore, the teacher's creative activity becomes the main direction of the implementation of modernization reforms in education and one of the essential directions of the transition to the model of innovative development. The use of modern information technologies has a qualitative effect on the professional activity of the teacher, as a result of which he acts as an intellectual, an organizer of the educational process and a translator of the necessary knowledge, skills and abilities. The introduction of modern pedagogical technologies into the educational process requires the teacher to have serious preparation and confident mastery of the material, a clear understanding of how to perform this task, prompts him to show creative initiative, creating a basis for the full-fledged development of a creative personality, thus contributing to the acquisition of advanced pedagogical experience for quality preparation of students for future professional activities.
Scaling programmable silicon photonics circuits

We give an overview the progress of our work in silicon photonic programmable circuits, covering the technology stack from the photonic chip over the driver electronics, packaging technologies all the way to the software layers. On the photonic side, we show our recent results in large-scale silicon photonic circuits with different tuning technologies, including heaters, MEMS and liquid crystals, and their respective electronic driving schemes. We look into the scaling potential of these different technologies as the number of tunable elements in a circuit increases. Finally, we elaborate on the software routines for routing and filter synthesis to enable the photonic programmer.
A case weighted similarity deep measurement method based on a self-attention Siamese neural network

Abstract To improve the accuracy of similarity measures in case-based reasoning, in this paper, we propose a deep metric learning method based on a self-attention mechanism and a Siamese neural network to realize the weighted similarity measure between cases. In this method, weight assignment and similarity measurement processes are integrated into a deep network. The method can map cases to a new feature space through nonlinear processing of the network layer to obtain better feature representation. The inner relationship between the features is captured by the self-attention mechanism, which is connected to the previous network layer, and the weight of the features is determined by the scoring function. Finally, a metric function is added to the contrastive loss to measure the case similarity. Experiments show that the accuracy of this method is better than that of other algorithms in the similarity measure and can improve the accuracy of case retrieval.
Smart Knowledge Submission System Based on Natural Language Processing (NLP) Leveraging on Language Modelling Approach

Abstract Having a reliable classification of knowledge assets in a corporate knowledge base system in an oil and gas company is challenging. This is due to the tremendous amount and variety of knowledge assets generated by complex business and engineering processes. Submission of the knowledge assets is one of the main challenges as users may choose the wrong knowledge type or classification. A lengthy manual review process is required to evaluate the submission and some need to be rejected and returned to users. Incorrect classification of knowledge assets may cause issues in knowledge search engines as users cannot find correct articles due to the misclassification of the knowledge assets. In another challenge, some assets knowledge submission is duplicated as other users have submitted the same assets. Our research evaluates the potential of Natural Language Processing (NLP) applications in the form of neural language modeling transformers. The models will generate the following functions: Knowledge categorization: Automatically categorizes the knowledge items based on the given abstract. This will reduce the possibility of the wrong classification that can prevent the knowledge from being published, simplify knowledge submission and expedite the knowledge review process.Text Summarization: Automatically generating a summary of knowledge assets that can be used as short descriptions or abstracts during knowledge submission.Document Semantic Similarity: Identifies similar documents in terms of the context that can help for document search or deletion of duplicate documents
Efficient anisotropic scaling and translation invariants of Tchebichef moments using image normalization

Anisotropic scaling and translation invariants of Tchebichef moments are commonly used in image analysis to address issues arising from patterns distorted by non-uniform scaling and translation. In this paper, we formulate a new fast computational algorithm and a new normalization scheme based on the properties of Tchebichef moments. The proposed model can correctly resolve the mirror reflection ambiguities caused by the scaling transformations and gives smaller size deviations for various patterns so that the canonical form can easily fit within the normalized space. An empirical study shows that the proposed method significantly improves numerical computation and classification accuracy under non-noisy and noisy conditions when compared with existing methods. The main contribution of this paper is a novel fast computational algorithm for anisotropic scaling and translation invariants of Tchebichef moments and a new normalization scheme that produces features with higher discriminative power. The proposed algorithm is useful for recognizing objects with non-uniform size and displacement deformations. It is also a key component in formulating a better affine invariant algorithm for the image analysis community.
Partially observable collaborative model for optimizing personalized treatment selection

Precision medicine that enables personalized treatment decision support has become an increasingly important research topic in chronic disease care. The main challenges in designing a treatment algorithm include modeling individual disease progression dynamics and designing adaptive treatment selection strategy. This study aims to develop an adaptive treatment selection framework tailored to an individual patient’s disease progression pattern and treatment response. We propose a Partially Observable Collaborative Model (POCM) to capture the individual variations in a heterogeneous population and optimize treatment outcomes in three stages. The POCM first infers the disease progression models by subgroup patterns using population data in stage one and then fine-tunes the models for individual patients with a small number of treatment trials in stage two. In stage three, we show how the treatment policies based on the Partially Observable Markov Decision Process (POMDP) can be tailored to individual patients by utilizing the disease models learned from the POCM. Using a simulated population of chronic depression patients, we show that the POCM can more accurately estimate the personal disease progression than the traditional method of solving a hidden Markov model. We also compare the POMDP treatment policies with other heuristic policies and demonstrate that the POCM-based policies give the highest net monetary benefits in majority of parameter settings. To conclude, the POCM method is a promising approach to model the chronic disease progression process and recommend a personalized treatment plan for individual patients in a heterogeneous population.
Motif-based Graph Attention Network for Web Service Recommendation

Deep learning on graphs and especially graph convolutional networks (GCNs) have shown superior performance in collaborative filtering. Most GCNs have a message-passing architecture that enables nodes aggregate information from neighbours continuously through multiple layers. This also leads to a common issue called over-smoothing: as the number of layers increases, the node embeddings become similar and the performance of tasks such as link prediction degrades severely. In this work, we propose motif-based graph attention network for web service recommendation (MGSR) that alleviates the over-smoothing issue by incorporate network motifs in layer propagation. Extensive experiments show that our model outperforms state-of-the-art approaches.
Using CTI Data to Understand Real World Cyberattacks

The forensic analysis of Cyber Threat Intelligence (CTI) data is of capital importance for businesses and enterprises to understand what has possibly gone wrong in a cybersecurity system. Moreover, the fast evolution of the techniques used by cybercriminals requires collaboration among multiple partners to provide efficient security mechanisms. STIX has emerged as the industrial standard to share CTI data in a structured format, allowing entities from over the world to exchange information to broaden the knowledge base in the area. In this work, we shed light on the type of information contained in these datasets shared among partners. We analyze a large real-world STIX dataset and identify trends for the reporting of CTI data. Then, we deep dive into two kinds of attack patterns found in the dataset: Command & Control and Malicious Software Download. We found the data is not only useful for forensic analysis but can also be used to improve the protection against new attacks.
Crises of reputation as asynchronous online polylogues

Abstract Crises of reputation are rarely studied from the linguistic point of view, which results in several research gaps. Therefore, in this study three selected crisis cases have been analyzed as asynchronous online polylogues —Internet debates performed by stakeholders in different sources from the moment of publication. In this approach corpus analysis has been combined with the theory of computer-mediated communication, speech acts and a semantic study of emotions. One of the findings was that certain common patterns of subtopics, emotion expressions and communicative actions are noticeable in the user-generated content across different crisis cases. Contrary to some NLP studies, emotions expressed by stakeholders in a crisis situation refer not to the major topic, but to its subtopics, which impacts the construction of text-mining models. Unlike in pre-social media studies, sarcasm and not anger is the major emotion expressed textually in reaction to a crisis of reputation.
Investigating Changes in Hand-Eye Coordination and Reaction Time Using a VR Squash Simulation

Hand-eye coordination and reaction time are essential for many sports and are associated with cognitive health. Training hand-eye coordination is hence important but can be limited by lack of equipment, suitable facilities, instructors, or training partners. In this research, we have designed a VR squash game for training hand-eye coordination at home, and we investigate users’ perceptions of the tool and how it compares with traditional training.
TurBO: A cost-efficient configuration-based auto-tuning approach for cluster-based big data frameworks

Big data processing frameworks such as Spark usually provide a large number of performance-related configuration parameters, how to auto-tune these parameters for a better performance has been a hot issue in academia as well as industry for years. Through delicately tradeoff between exploration and exploitation, Bayesian Optimization (BO) is currently the most appealing algorithm to achieve configuration auto-tuning. However, considering the tuning cost constraint in practice, there are three critical limitations preventing conventional BO-based approaches from being directly applied into auto-tuning cluster-based big data frameworks. In this paper, we propose a cost-efficient configuration auto-tuning approach named TurBO for big data frameworks based on two enhancements of vanilla BO:1) To reduce the essential iteration times, TurBO integrates a well-designed adaptive pseudo point mechanism with BO; 2) To avoid the time-consuming practical evaluation of sub-optimal configurations as possible, TurBO leverages the proposed CASampling method to intelligently tackle with these sub-optimal configurations based on ensemble learning with historical tuning experiences. To evaluate the performance of TurBO, we conducted a series of experiments on a local Spark cluster with 9 different HiBench benchmark applications. Overall, compared with 3 representative BO-based baseline approaches OpenTuner, Bliss and ResTune, TurBO is able to speedup the tuning procedures respectively by 2.24×, 2.29× and 1.97× on average. Besides, TurBO can always achieve a positive cumulative performance gain under the simulated dynamic workload scenario, which means TurBO is indeed appropriate for workload changes of big data applications.
Multi-resolutional human visual perception optimized pathology image progressive coding based on JPEG2000

As one of the most studied areas, telemedicine allows doctors and pathologists to share their intelligence all over the world. To achieve the highest efficiency of remote pathology image browsing, especially for the whole-slide pathology image (WSI) big data, we develop a method to conduct the image coding based on the human visual quality for multiple displaying resolutions. In this paper, we establish a multi-resolutional human visual sensitivity model of Wavelet transform coefficients, based on the JPEG2000 standard and the human visual quality characters. With the analysis of the visual masking effect, we further propose a visibility threshold (VT) model for the human visual sensitivity optimized image coding of the WSIs, which considers multi-resolution and different visual qualities jointly. The proposed image coding methods with multiple quality or resolution layers can also be implemented for progressive image transmission and decoding. Compared with the conventional MSE optimized image coding algorithm, the decoded pathology image quality obtained from certain amounts of coded data using the proposed algorithm was proved to be improved from the aspect of visual perception through both objective and subjective evaluations. The perception-optimized progressive pathology image transmission and decoding were also verified with the combination of the proposed VT model and the JPIP protocol. With the proposed method, a remote medical server can adaptively transmit the image data according to the client’s subjective requirements for the remote browsing of pathology image big data, leading to improved efficiency of diagnosis in telemedicine.