Reviewerly/exHarmony huggingface dataset:
An error-tolerant approach for efficient AES key retrieval in the presence of cacheprefetching – experiments, results, analysis

The challenge in cache-based attacks on cryptographic algorithms is not merely to capture the cache footprints during their execution but to process the obtained information to deduce the secret key. Our principal contribution is to develop a theoretical framework based upon which our AES key retrieval algorithms are not only more efficient in terms of execution time but also require up to 75% fewer blocks of ciphertext compared with previous work. Aggressive hardware prefetching greatly complicates access-driven attacks since they are unable to distinguish between a cache line fetched on demand versus one prefetched and not subsequently used during a run of a victim executing AES. We implement a multi-threaded spy code that reports accesses to the AES tables at the granularity level of a cache block. Since prefetching greatly increases side-channel noise, we develop sophisticated heuristics to “clean up” the input received from the spy threads. Our key retrieval algorithms process the sanitized input to recover the AES key using only about 25 blocks of ciphertext in the presence of prefetching and, stunningly, a mere 2–3 blocks with prefetching disabled. We also derive analytical models that capture the effect of varying false positive and false negative rates on the number of blocks of ciphertext required for key retrieval.
Effect of the Noise on Generalized Peres Gate Operation

The advantages of quantum computing may be lost due to the presence of various noises in real quantum devices. In this paper, generalized Peres gates are studied using a solid-state model of a linear chain of atoms with nuclear spin one half, which is implanted in a spin-free silicon matrix. The effect of frequency noise on the correctness of gates operation is investigated on the example of the one- and two-step algorithmic transitions. It is shown for the first time that an imbalance by the magnitude of the resonance control frequency leads to a significant decrease in the fidelity of correct gate operation on the digital states. While for the superposition input signals stabilization of the fidelity is observed to a level of 0.4–0.8 with an increase in the imbalance of the resonance frequency. A similar effect takes place at time deviation of the frequency around the resonant value. Minimum values of the frequency imbalance parameters, which ensure correctness of the generalized Peres gate operation, are proposed.
Adversarial Authorship Attribution in Open-Source Projects

Open-source software is open to anyone by design, whether it is a community of developers, hackers or malicious users. Authors of open-source software typically hide their identity through nicknames and avatars. However, they have no protection against authorship attribution techniques that are able to create software author profiles just by analyzing software characteristics. In this paper we present an author imitation attack that allows to deceive current authorship attribution systems and mimic a coding style of a target developer. Withing this context we explore the potential of the existing attribution techniques to be deceived. Our results show that we are able to imitate the coding style of the developers based on the data collected from the popular source code repository, GitHub. To subvert author imitation attack, we propose a novel author obfuscation approach that allows us to hide the coding style of the author. Unlike existing obfuscation tools, this new obfuscation technique uses transformations that preserve code readability. We assess the effectiveness of our attacks on several datasets produced by actual developers from GitHub, and participants of the GoogleCodeJam competition. Throughout our experiments we show that the author hiding can be achieved by making sensible transformations which significantly reduce the likelihood of identifying the author's style to 0% by current authorship attribution systems.
PSO algorithm for UAV autonomous path planning with threat and energy cost optimization

The PSO (Particle Swarm Optimization) algorithm uses a population of single particles, randomly distributed over the function search space, in order to construct an optimal flight path. During the algorithm iteration execution, each particle evaluates its fitness function according to the actual position. Then, every particle moves towards a direction dependent on its actual best position and the position of the best particle so far. The procedure ends when the iteration limit is reached or fitness criterium is met. In the presented autonomous UAV 3D path planning method, a PSO algorithm is used to build a feasible path, optimized in terms of fuel and threat cost. The process begins when the UAV detects or receives information about threats appearing on its primary trajectory. The population of particles moves in the search space of three variables, which are the angles of pitch, roll and yaw. These angles determine the spatial orientation of the UAV, indicating its direction of movement in each step. Further waypoints are chosen with consideration of the distance to the target and violation of threat areas. As a final result, the PSO algorithm constructs a suboptimal, feasible path which could be used as a reference trajectory for the UAV’s automatic control system. Simulation results turned out to be completely repeatable and indistinguishable, despite the stochastic nature of the algorithm, which proves its great optimization abilities. Moreover, its short execution time (within seconds) allows this procedure to be used in real time applications.
Prosthesis and the engineered imagination: reading augmentation and disability across cultural theory, representation and product design

This article argues for the value of considering the interaction of literary/cultural studies, disability studies and engineering/design studies in the ongoing development of a critical medical humanities research frame. With a specific focus on prosthesis, but also considerations of embodiment, technology and augmentation as concepts in both cultural/disability theory and engineering/design, we note how the shifting and plastic ideas of ‘the prosthetic’ as used within cultural studies have never been in conversation with scholars who work on prostheses in engineering design or the processes through which such technologies are produced. Additionally, we show that the increased use of systems engineering in the design and construction of prostheses creates fractured ideas of disabled bodies that frequently ignore both the cultural meaning and lived experience of technology use. In design and engineering, prostheses are literal objects, often made to order for a diverse range of clients and produced across different working platforms; in cultural studies, the word creates multiple resonances around both augmented bodies and non-embodied states increasingly understood in terms of assemblage and supplementarity. Working from this, we outline how questions of metaphor, materiality and systems weave through the different disciplines. The article claims that a critical dialogue between the working methods of literary/cultural studies and engineering/design, for all their obvious differences, possesses the potential to create informed and sophisticated accounts of disability embodiment. Our conclusion brings the strands of the enquiry together and points to the merits of engineering the imagination, and imagining engineering, as both a subject and method in future medical humanities research.
Research on Library functional layout based on Intelligent occupying system

At present, university libraries are using the intelligent management system of library public seats introduced by our library, that is, the reservation system of public space seats. With the increase of the enrolment rate of higher education in our country, the demand for intelligent management in university libraries will become more and more important, but based on the nature of each university and the demand for public resources. Therefore, I hope that through this paper to introduce some experience of the intelligent seat management system of University libraries, to find a more suitable operating scheme for the actual situation of the library, and as a breakthrough to achieve library functional zoning and planning.
MCDM for Candidate Selection in E-Voting

E-voting is one of the most important components of e-democracy and forms the basis of a democratic governance system. Voting results always lead to a broad debate in terms of candidate selection and whether the candidate elected to a position is suitable for that position. At present, the selection of qualified personnel and their appointment to responsible positions in public administration is one of the topical issues. In this article is proposed an MCDM for selection of candidates in e-voting. Criteria for the candidates' selection are determined and the relation of each candidate to other candidates is assessed using a binary matrix. Candidate rating is calculated according to all criteria. A numerical experiment is provided for candidate evaluation on the base of the selected criteria and ranked according to the importance of the criteria. The proposed model allows selecting a candidate with competencies based on the criteria set out in the e-voting process and making more effective decisions.
Image Recommendation for Automatic Report Generation using Semantic Similarity

Automatic report generation is a technology that automatically generates documents in the form of report by summarizing various materials according to a specific topic in time sequence or subject. Although the main content of the report is text, insertion of appropriate images can improve the completeness of the report. In this paper, we propose an image recommendation method for automatically selecting and inserting appropriate images corresponding to a specific part of a report. In our proposed method, reevaluation of the candidate images is performed based on the semantic similarity between query and the contents of the images. In order to transform semantic information of text query and image into one vector space, we extracted semantic information from image as a set of tags form using deep learning based object detection module. Also, we extracted tags from the given title of the image so that the proposed system can evaluate the candidate images even in the case that the given query includes specific keywords or proper nouns which were not learned by object detection and recognition module in advance. In this paper, we conducted experiments on eight queries related to recent events to verify the applicability of our proposed image recommendation system and evaluate the image selection accuracy.
Automatic 3D Registration of CT-MR Head and Neck Images With Surface Matching

Radiotherapy is a major treatment for head and neck cancer. Currently, computed tomography (CT) is utilized to delineate the target area and make radiotherapy plans. Compared with CT images, magnetic resonance (MR) images have excellent soft tissue contrast, which can distinguish normal surrounding tissues. It is necessary to register MR and CT images since it helps physicists to improve the accuracy of radiotherapy plans. Most of the current registrations require manual intervention to select regions of interest, which increases the workload of doctors and the time of registration to a certain extent. In this paper, an automatic registration method is proposed to delineate the regions of interest. Herein, surface meshes are extracted from the CT and MR images and utilized to perform the surface matching, then the regions of interest are extracted automatically by calculating the overlapped regions between the surface mesh of the surface-aligned MR images and CT images. Furthermore, a multi-level resolution registration mechanism is utilized to improve the registration speed. Surface matching is performed using low-resolution images to obtain transformation parameters as initial parameters for rigid registration followed by deformable registration. The experiments demonstrate that our proposed method performs better in the registration speed and accuracy over the conventional methods of delineating regions of interest manually.
Development of a new dual‐pattern fringe projection scanner

Abstract This paper proposes a new fringe projection scanner that uses a dual pattern to prevent phase retrieval errors or complications. The pattern comprises two horizontal and vertical patterns, however, just one of them is involved in computing the coordinates of an object point. To define the pattern, a photometric stereo component is used to extract the geometric properties of the object. The extracted data is employed to form a decision mask, each pixel of which defines the best pattern (horizontal, vertical or both) related to one of the object points. In this way, the scanner determines which pattern to use to obtain the most accurate results. Experiments suggest not only the avoidance of phase retrieval problems, but also an increase in the accuracy of the measurements (by 56 % in this test).
Custom-made Anonymization by Data Analysis Program Provided by Recipient

Anonymization is a method used in privacy-preserving data publishing. Previous studies show that anonymization based on the request of a data recipient, the priority of attributes, helps to maintain data utility. However, it is difficult for recipients to generate requests because they can not know which attribute important without data analysis. To address this issue, we propose a framework for performing custom-made anonymization by data analysis program provided by recipient. This enables the recipient to generate a request after creating a program and performing an indirect analysis of an original dataset by the program. Moreover, we describe an inference attack model for this framework and propose a secure method for restraining such an attack.
Digital Preservation of Indigenous Culture and Narratives from the Global South: In Search of an Approach

This research seeks to digitally preserve cultural histories and artifacts, which are practiced/produced in the underserved indigenous spaces of rural eastern India. This paper is a case study of co-developing Sangraksha—a digital humanities application. The application seeks to facilitate the process of writing history from the below by underrepresented populations at the margins. The villages in this research were geographically remote and socio-economically underdeveloped. The research populations represented individuals who possessed low levels of literacy, limited language proficiency in English and mainstream Indic languages (e.g., Hindi and Bengali), as well as limited familiarity with computers and computing environments. Grounded in long-term ethnographic engagements in the remote Global South, this study explored a range of cultural, aesthetic, and contextual factors that were instrumental in shaping and co-generating digital humanities solutions for under-researched international populations. On one hand, the research initiative sought to co-create a culturally meaningful and welcoming digital environment to make the experience contextually appropriate and user-friendly. On the other hand, grounded in visual and sensory methodologies, this research used community generated imageries and multimedia (audio, photographs and audio-visual) to make the application inclusive and accessible. Moreover, the application-development attempt also paid close attention to intercultural, local-centric, community-driven co-design aspects to make the approach socially-embedded and sustainable in the long term.
Chinese Calligraphy

Chinese Calligraphy have many different styles. Usually beginners will focus on one style and learn it. However, beginners cannot accurately determine the type of Chinese calligraphy. Especial for the western foreigner. Therefore, it has important significance that using a computer to assist in collecting the calligraphy samples and do the correct classification. In addition, it can help some non-speak Chinese library in the classification and identification with some books. It also improves the efficiency that librarian work for some classification with Chinese Calligraphy books.
Chinese Story Generation with FastText Transformer Network

The sequence transformer models are based on complex recurrent neural network or convolutional networks that include an encoder and a decoder. High-accuracy models are usually represented by used connect the encoder and decoder through an attention mechanism. Story generation is an important thing. If we can let computers learn the ability of story-telling, computers can help people do more things. Actually, the squence2squence model combine attention mechanism is being used to Chinese poetry generation. However, it difficult to apply in Chinese story generation, because there are some rules in Chinese poetry generation. Therefore, we trying to use 1372 human-labeled summarization of paragraphs from a classic novel named “Demi-Gods and Semi-Devils” to train the transformer network. In our experiment, we use FastText to combine Demi-Gods and Semi-Devils Dataset and A Large Scale Chinese Short Text Summarization Dataset to be input data. In addition, we got a lower loss rate by using two layer of self-attention mechanism.
Artificial Intelligence in Future Evolution of Mobile Communication

With the completion of the first release (Rel-15) of the 3GPP fifth-generation (5G) NR specifications [1, 2], the research community should now direct its focus towards the next step in the evolution of wireless mobile communication. Similar to earlier generations, it can be expected that the next ten years will see a gradual evolution of NR, introducing new innovative technology components and further enhancing the capabilities and expanding the scope of 5G wireless access. In a longer-time perspective, we may see the emergence of completely new “beyond 5G” radio-access technology.
Towards a Taxonomy of Software Maintainability Predictors

Software maintainability prediction has gained more attention in the last decade. Several studies have conducted empirical studies to look for models to predict software maintainability more accurately. In a previous work, a systematic mapping study (SMS) was performed in this context and a set of metrics used as predictors of software maintainability has been identified. But, unfortunately those metrics are not organized in a structured way. Moreover, some authors may raise the same metric with the same meaning, but with different wordings. Hence, it becomes a necessity to unify all the metrics in a taxonomy that will help researchers build maintainability models in an easy way. The proposed taxonomy is 3 levels with categories, subcategories and metrics. We expect that the use of this taxonomy by researchers can help us identify other options to both propose a more useful taxonomy and to perform its evaluation.
Fast detection of largest repeating layout pattern

Several tools in the mask data preparation flow utilize the repetitions of patterns within a layout to reduce processing time. Conventionally, this is achieved by an analysis of the design hierarchy. However, intermediate processing in the data preparation flow can distort the hierarchy. Also, some repetitions in small-grain structures may not be represented in the hierarchy. An alternative is to learn repeating patterns and their frequency by analyzing the layout as a flat data structure. This paper demonstrates a methodology for learning the largest repeating patterns in a layout without the use of hierarchy, purely by referencing the layout as a flat data structure. The experimental results show its efficacy for layouts containing hundreds of thousands of polygons, as well as its efficiency in terms of computing time and memory usage. The results indicate that it is practical to use a distributed process to directly learn the largest repeating patterns without resorting to design hierarchy, in a reasonable runtime and memory usage.
Dynamic Selection of Classifiers for Fusing Imbalanced Heterogeneous Data

Data fusion (DF) from multiple heterogeneous sources is a typical task for many multisensor applications including remote sensing classification problems. Multiple classifier systems (MCS) provide a natural way to solve DF on the decision level by training individual classifiers separately on its own data source and then combine their outputs. In this paper, we consider a dynamic selection (DS) framework to select and fuse competent classifiers of MCS. For this, we propose a competence estimation and selection method to improve the performance of the DF system especially under class imbalance. We evaluate the method with synthetic and real datasets, demonstrating the applicability of the proposed framework.
Multi swarm optimization based automatic ontology for e-assessment

The utilization of ontology in the e-assessment area has grown tremendously. The context of e-learning is significant to the students for educational purposes. This makes the testing process easy for the students and also for the teachers. The majority of the approaches that deals with the ontology issue have suggested that the individual ontology models have merely a fraction of the assessment domain. To trounce such drawbacks, here, an automated ontology creation is proposed for the e-assessment systems. Initially, the text is extracted from the web utilizing the Unsupervised Quick Reduct (UQR) algorithm. This is trailed by the summarization of the texts using the multi-swarm optimization (MSO) based on preference learning. Finally, the sentence of the summary is then transmuted to multiple choice questions (MCQ). The keys are created using statistical pattern (SP). The efficiency of the system is examined using the experimental outcomes like error rate, precision, recall and accuracy. In accuracy, the proposed UQR algorithm achieves 97.7%, MSO achieve 96.2% accuracy and key generation achieves 94.7% accuracy. The proposed automatic ontology system indicates better when weighed against the top-notch methods.
Fast algorithm for zero‐phase linear filter using discrete cosine transform

This Letter deals with a practical signal processing problem where the filter is zero-phase and is specified by the frequency response. The authors show that if the signal is symmetrically extended, then the fast Fourier transform (FFT)-based algorithm can be efficiently implemented by using the discrete cosine transform without signal extension. They extend the proposed algorithm to 2D filters and show that computationally it is at least four times more efficient than the FFT-based algorithm. A practical application of the new algorithm is a fast Wiener inverse filter, which is used to estimate images taken under the condition of air turbulence.
Computing the Identification Capability of SQL Queries for Privacy Comparison

Comparing SQL queries is an interesting area of research, with applications of query similarity in different domains; for instance, anomaly-based intrusion detection systems. Comparing two SQL queries in terms of privacy - privacy comparison as well as computing a quantitative value for the identification capability of an SQL query is desirable. In this paper, we compute the identification capability of an SQL query and subsequently we propose an approach to compare two SQL queries in terms of privacy by introducing the notion of privacy equivalence, less-private and more-private relations. Additionally, an edge-labelled directed acyclic graph style privacy-aware attribute relationship diagram is proposed that facilitates the privacy comparison.