Reviewerly/exHarmony huggingface dataset:
Personal Data Protection as a Tool to Fight Cyber Corruption

As digital technologies proliferate, personal data vulnerabilities enable new forms of systemic corruption. Robust data protection frameworks are essential safeguards, yet remain underutilized in anti-corruption efforts. This paper analyzes the complex intersection between privacy, cyber-security and corruption. Rapid technological change has led to exponential growth in personal data generation. However, legal and ethical oversight lags behind. Vast troves of user data are harvested, often without full consent or transparency, creating information asymmetries ripe for abuse. Data may be exploited, manipulated, or weaponized to enable digital authoritarianism, cybercrime, discrimination, elite capture, and other corrupt ends. Users lack control over or visibility into data misuse once obtained. Case examples showcase vulnerabilities across sectors. Tighter constraints on data collection, use and sharing, coupled with oversight and accountability measures, can help rein in these risks. While data protection principles increasingly shape global governance frameworks, considerable implementation and enforcement gaps persist. Integrating privacy into anti-corruption programs as a core pillar, alongside transparency and ethics initiatives, is vital to secure the data flows underpinning digital societies against corrupt interests.
Salient Object Detection in Optical Remote Sensing Images Driven by Transformer

Existing methods for Salient Object Detection in Optical Remote Sensing Images (ORSI-SOD) mainly adopt Convolutional Neural Networks (CNNs) as the backbone, such as VGG and ResNet.Since CNNs can only extract features within certain receptive fields, most ORSI-SOD methods generally follow the local-to-contextual paradigm.In this paper, we propose a novel Global Extraction Local Exploration Network (GeleNet) for ORSI-SOD following the global-to-local paradigm.Specifically, GeleNet first adopts a transformer backbone to generate four-level feature embeddings with global long-range dependencies.Then, GeleNet employs a Direction-aware Shuffle Weighted Spatial Attention Module (D-SWSAM) and its simplified version (SWSAM) to enhance local interactions, and a Knowledge Transfer Module (KTM) to further enhance cross-level contextual interactions.D-SWSAM comprehensively perceives the orientation information in the lowest-level features through directional convolutions to adapt to various orientations of salient objects in ORSIs, and effectively enhances the details of salient objects with an improved attention mechanism.SWSAM discards the directionaware part of D-SWSAM to focus on localizing salient objects in the highest-level features.KTM models the contextual correlation knowledge of two middle-level features of different scales based on the self-attention mechanism, and transfers the knowledge to the raw features to generate more discriminative features.Finally, a saliency predictor is used to generate the saliency map based on the outputs of the above three modules.Extensive experiments on three public datasets demonstrate that the proposed GeleNet outperforms relevant state-of-the-art methods.The code and results of our method are available at https://github.com/MathLee/GeleNet.
An analysis on Teacher Talk using Flanders Interaction Analysis Categories (FIAC)

The problem of this research was some lecturers only used one categories of teacher talks. The aims of this study were to identify the characteristics and the categories of teacher talk in the classroom using Flanders Interaction Analysis Categories System (FIACS). This study was case study design and qualitative method. The subject of this study was one novice and senior lecturer at IAIN Bengkulu. It used Flanders Interaction Analysis Categories System (FIACS) to analyze teacher’s talk. The researcher used observation and interview for collecting the data. The result both of novice and senior lecturer showed that the characteristics of teacher is content cross and the most frequent is indirect talk. The percentage of teacher characteristics of novice lecturer was 45% while the percentage of teacher characteristics of senior lecturer was 39%. Novice lecturer used asking question and lecturing as the most frequent in indirect and direct talks. The percentage of asking question and lecturing were 39% and 21.5%. In other sides, senior lecturer used asking question and giving direction as the most frequent in indirect and direct talks. The percentage of asking and giving direction were 27.5% and 18.5%. Nevertheless, the occurred categories happened naturally with the consideration of learning situation that takes place. In addition the analysis of teachers’ talk in the classroom would be beneficial for teachers to plan and conduct enhanced learning situation. It concluded teacher talks of novice and senior lecturer in the classroom using Flanders Interaction Analysis Categories (FIAC).
CenterNet++ for Object Detection

There are two mainstream approaches for object detection: top-down and bottom-up. The state-of-the-art approaches are mainly top-down methods. In this paper, we demonstrate that bottom-up approaches show competitive performance compared with top-down approaches and have higher recall rates. Our approach, named CenterNet, detects each object as a triplet of keypoints (top-left and bottom-right corners and the center keypoint). We first group the corners according to some designed cues and confirm the object locations based on the center keypoints. The corner keypoints allow the approach to detect objects of various scales and shapes and the center keypoint reduces the confusion introduced by a large number of false-positive proposals. Our approach is an anchor-free detector because it does not need to define explicit anchor boxes. We adapt our approach to backbones with different structures, including ‘hourglass’- like networks and ‘pyramid’- like networks, which detect objects in single-resolution and multi-resolution feature maps, respectively. On the MS-COCO dataset, CenterNet with Res2Net-101 and Swin-Transformer achieve average precisions (APs) of 53.7% and 57.1%, respectively, outperforming all existing bottom-up detectors and achieving state-of-the-art performance. We also design a real-time CenterNet model, which achieves a good trade-off between accuracy and speed, with an AP of 43.6% at 30.5 frames per second (FPS). The code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/Duankaiwen/PyCenterNet</uri> .
Hidden Signature for DTW Signature Verification in Authorizing Payment Transactions

Traditional use of dynamic time warping for signature verification consists of forming some dissimilarity measure between the signature in question and a set of “template signatures”. In this paper, we propose to replace this set with the hidden signature and use it to calculate the normalized errors of signature under verification. The approach was tested on the MCYT database, using both genuine signatures and skilled forgeries. Moreover, we present the real-world application of the proposed algorithm, namely the complete biometric system for authorizing payment transactions. The authorization is performed directly at a point of sale by the automatic signature verification system based on the hidden signature.
Ontology Creation Process in Knowledge Management Support System for a Research Institute

Though the issue of knowledge management is a hot subject of interest in nowadays market companies, integrated solutions fit to the specific needs of research institutes still require more attention. This paper documents a part of the research activities performed at National Institute of Telecommunications, related to development of research institute knowledge management support system. The ideas lying in the background of the system come from the recent theories of knowledge creation and creativity support and from experience with everyday practice of knowledge management in market companies. Main focus is put here on the issue of creation of a research topics ontology that is meant to be a semantic backbone of the system. Three-stage approach is proposed, aiming at the construction of ontologies for different levels of organizational hierarchy, from individual researcher, through group or unit, up to the whole institute. Created ontologies are linked to knowledge resources and support diverse activities performed at those levels
The Coordination Pyramid: A Perspective on the State of the Art in Coordination Technology

Most frameworks that categorize technology for collaborative software development look at only one aspect of coordination support. The Coordination Pyramid classifies technologies according to multiple coordination paradigms, offering a unified, complementary perspective and a structure for evaluating emerging technology.
Hand Gesture Recognition and Voice Conversion System for Dump People

Sign language plays a major role for dumppeople to communicate with normal people. It is very difficult for mute people to convey their message to normal people. Since normal people are not trained on hand sign language. In emergency time conveying their message is very difficult. So the solution for this problem is to convert the sign language into human hearing voice. There are two major techniques available to detect hand motion or gesture such as vision and non-vision technique and convert the detected information into voice through raspberry pi. In vision based technique camera will be used for gesture detection and non-vision based technique sensors are used. In this project non-vision based technique will be used. Most of the dumb people are deaf also. So the normal people's voice can be converted into their sign language. In an emergency situation the message will automatically send to their relation or friends.
A Mental Model for a Rhetorical Arguer

This paper proposes a general mental model for an agent capable to engage in rhetorical argumentation. The model is grounded in works in the philosophy of mind, and is inspired by research in contextual reasoning. The issues that are dealt with in this paper are mainly ontological, as we leave aside the problem of knowledge representation and reasoning, and concentrate on the conceptualisation of the model.
Development of Metamodel for Information Security Risk Management

Information technology and information systems have been used widely in many fields, such as business, education, marketing, transportation, medical, and many other areas. In the information technology and system field, a security aspect plays a vital role and thus becomes a challenging issue. Therefore, security should be ready installed and resistant to various numbers of potential attacks. In Information Security and Information Technology, deciding what countermeasures could potentially harm the organization from achieving its business objectives is important. Reducing risk to an acceptable level is the main target of the risk management process. On the other hand, the main reason for failure in Information Security Risk Management (ISRM) is the complexity and inflexibility of the existing models. Domain modulars usually spend a lot of time understanding the nature of the domain they desire to model. Even though many current ISRM models appear, finding a suitable model that could provide a straight guideline to the ISRM users based on their problems is limited. To overcome this issue, this book chapter follows the design science research to create a generic metamodel that can describe the semantics of ISRM models and their solutions through one unified model. Through the metamodel, various risk management problems faced by different levels of ISRM users can be solved based on the problem attributes, such as risk determination specific to a firewall vulnerability problem and risk assessment for an information security project management. This can help many users/newcomers to this domain to easily understand the concepts required for their own information security risk problems.
High-Availability at Massive Scale: Building Google’s Data Infrastructure for Ads

Google's Ads Data Infrastructure systems run the multi-billion dollar ads business at Google. High availability and strong consistency are critical for these systems. While most distributed systems handle machine-level failures well, handling datacenter-level failures is less common. In our experience, handling datacenter-level failures is critical for running true high availability systems. Most of our systems (e.g. Photon, F1, Mesa) now support multi-homing as a fundamental design property. Multi-homed systems run live in multiple datacenters all the time, adaptively moving load between datacenters, with the ability to handle outages of any scale completely transparently. This paper focuses primarily on stream processing systems, and describes our general approaches for building high availability multi-homed systems, discusses common challenges and solutions, and shares what we have learned in building and running these large-scale systems for over ten years.
Mining the Enriched Subgraphs for Specific Vertices in a Biological Graph

In this paper, we present a subgroup discovery method to find subgraphs in a graph that are associated with a given set of vertices. The association between a subgraph pattern and a set of vertices is defined by its significant enrichment based on a Bonferroni-corrected hypergeometric probability value. This interestingness measure requires a dedicated pruning procedure to limit the number of subgraph matches that must be calculated. The presented mining algorithm to find associated subgraph patterns in large graphs is therefore designed to efficiently traverse the search space. We demonstrate the operation of this method by applying it on three biological graph data sets and show that we can find associated subgraphs for a biologically relevant set of vertices and that the found subgraphs themselves are biologically interesting.
An Integrated Framework for Mining Temporal Logs from Fluctuating Events

The importance of mining time lags of hidden temporal dependencies from sequential data is highlighted in many domains including system management, stock market analysis, climate monitoring, and more. Mining time lags of temporal dependencies provides useful insights into the understanding of sequential data and predicting its evolving trend. Traditional methods mainly utilize the predefined time window to analyze the sequential items, or employ statistical techniques to identify the temporal dependencies from a sequential data. However, it is a challenging task for existing methods to find the time lag of temporal dependencies in the real world, where time lags are fluctuating, noisy, and interleaved with each other. In order to identify temporal dependencies with time lags in this setting, this paper comes up with an integrated framework from both system and algorithm perspectives. Specifically, a novel parametric model is introduced to model the noisy time lags for temporal dependencies discovery between events. Based on the parametric model, an efficient expectation maximization approach is proposed for time lag discovery with maximum likelihood. Furthermore, this paper also contributes an approximation method for learning time lag to improve the scalability in terms of the number of events, without incurring significant loss of accuracy.
Robust Performance-Based Resource Provisioning Using a Steady-State Model for Multi-Objective Stochastic Programming

Cloud computing has enabled entirely new business models for high-performance computing. Having a dedicated local high-performance computer is still an option for some, but more are turning to cloud computing resources to fulfill their high-performance computing needs. With cloud computing it is possible to tailor your computing infrastructure to perform best for your particular type of workload by selecting the correct number of machines of each type. This paper presents an efficient algorithm to find the best set of computing resources to allocate to the workload. This research is applicable to users provisioning cloud computing resources and to data center owners making purchasing decisions about physical hardware. Studies have shown that cloud computing machines have measurable variability in their performance. Some of the causes of performance variability include small changes in architecture, location within the datacenter, and neighboring applications consuming shared network resources. The proposed algorithm models the uncertainty in the computing resources and the variability in the tasks in a many-task computing environment to find a robust number of machines of each type necessary to process the workload. In addition, reward rate, cost, failure rate, and power consumption can be optimized, as desired, to compute Pareto fronts.
Optimizing Semantic Annotations for Web Service Invocation

Semantic annotations play an important role in semantics-aware service discovery, recommendation and composition. While existing approaches and tools focus on facilitating the development of semantic annotations on web services, the validation of the quality of annotations is largely overlooked. Meanwhile, the refinement of semantic annotations mostly goes through manual processes, which not only is time-consuming but also requires significant domain knowledge. To enhance the Quality of Semantic Annotation (QoSA), we have developed a technique to incrementally assess and correct semantic annotations of web services. Aiming at supporting web service interoperation, we have formalized the QoSA of input and output parameters. Based on such formalism, test cases are automatically generated to validate service annotations. Learned semantic instances are then accumulated to iteratively validate semantic annotations of other services. Furthermore, a three-phase optimization methodology including local-feedback, global-feedback, and global-propagate is developed to improve the QoSA by incrementally correcting inaccurate annotations. Experiments over a real-world web services repository have demonstrated that our technique can effectively improve QoSA of services, gaining a 78.68 percent improvement in input parameters annotations and identifying 36.47 percent inaccurate output parameters annotations. The proposed technique can be equipped at various service repositories to enhance service discovery and recommendation.
Towards Automated Choreographing of Web Services Using Planning

For Web service composition, choreography has recently received great attention and demonstrated a few key advantages over orchestration such as distributed control, fairness, data efficiency, and scalability. Automated design of choreography plans, especially distributed plans for multiple roles, is more complex and has not been studied before. Existing work requires manual generation assisted by model checking. In this paper, we propose a novel planning-based approach that can automatically convert a given composition task to a distributed choreography specification. Although planning has been used for orchestration, it is difficult to use planning for choreography, as it involves decentralized control, concurrent workflows, and contingency. We propose a few novel techniques, including compilation of contingencies, dependency graph analysis, and communication control, to handle these characteristics using planning. We theoretically show the correctness of this approach and empirically evaluate its practicability.
Nuclei and automorphism groups of generalized twisted Gabidulin codes

Generalized twisted Gabidulin codes are one of the few known families of maximum rank metric codes over finite fields.As a subset of m × n matrices, when m = n, the automorphism group of any generalized twisted Gabidulin code has been completely determined by the authors in [20].In this paper, we consider the same problem for m < n.Under certain conditions on their parameters, we determine their middle nuclei and right nuclei, which are important invariants with respect to the equivalence for rank metric codes.Furthermore, we also use them to derive necessary conditions on the automorphisms of generalized twisted Gabidulin codes.
Linear Logic Properly Displayed

We introduce proper display calculi for intuitionistic, bi-intuitionistic and classical linear logics with exponentials, which are sound, complete, conservative, and enjoy cut elimination and subformula property. Based on the same design, we introduce a variant of Lambek calculus with exponentials, aimed at capturing the controlled application of exchange and associativity. Properness (i.e., closure under uniform substitution of all parametric parts in rules) is the main technical novelty of the present proposal, allowing both for the smoothest proof of cut elimination and for the development of an overarching and modular treatment for a vast class of axiomatic extensions and expansions of intuitionistic, bi-intuitionistic, and classical linear logics with exponentials. Our proposal builds on an algebraic and order-theoretic analysis of linear logic and applies the guidelines of the multi-type methodology in the design of display calculi.
Transfer Learning for Multiagent Reinforcement Learning Systems

Learning to solve sequential decision-making tasks is difficult. Humans take years exploring the environment essentially in a random way until they are able to reason, solve difficult tasks, and colla
Privacy-Preserving Fine-Grained Data Retrieval Schemes for Mobile Social Networks

In this paper, we propose privacy-preserving fine-grained data retrieval schemes for mobile social networks (MSNs). The schemes enable users to retrieve data from other users who are interested in some topics related to a subject of interest. We define a subject to be a broad term that can cover many fine-grained topics, e.g., History can be a subject and World War I can be a topic. We consider centralized and decentralized network models. Our centralized scheme allows users to securely outsource data to a server such that the server matches the users who are interested in same topic(s) and have defined social attributes with privacy preservation. Searchable encryption scheme and a proposed cryptography construct are used to enable the server to match the topics and attributes without knowing any private information. By using the social attributes, users can prescribe the other users who can be connected to. We also propose a decentralized scheme that can be used when there is no connection to the server, i.e, shortage of Internet connectivity. The scheme leverages friends-of-friends relationship and transferable trust concept, where each user trusts his friends and the friends of friends. If a friend is not interested in the requested subject, he/she can link him/her to his/her friends without knowing the requested subject to preserve privacy. Our schemes use Bloom filters to store the topics of interest to reduce the storage and communication overhead. This is important because the number of fine-grained topics can be large. Different techniques to store the topics in the filter are proposed and investigated. Performance metrics are proposed and evaluated using real implementations. Our analysis and implementation results demonstrate that our schemes can preserve the privacy of the MSN users with high performance.