Reviewerly/exHarmony huggingface dataset:
Research on Tampering Detection of Material Gene Data Based on Fragile Watermarking

In order to timely and effectively discover the error or falsification of the data in the material gene database, a data tamper detection method based on the fragile watermarking technology is proposed. The method is divided into two stages: embedding and detection of fragile watermarks. In the embedding stage of the watermark, the original data is first grouped and the eigenvalues are extracted. On this basis, the watermark is generated and embedded. In the data tampering detection stage, the data is first grouped and the watermark is extracted, and then the watermark is calculated and detected. The experimental results show that the method can not only effectively detect the situation of data errors or tampering, but also accurately locate a tuple that has been tampered with.
Musicking with Music-Generation Software in <i>Virtutes Occultae</i>

This article explores concepts of compositional meaning that arise from cocreative composing with music-generation software. Drawing from an analysis of the 2017 electroacoustic composition Virtutes Occultae, the composer discusses the implications of computer-generated music for the role of the composer. After an overview of how the music-generation software he developed contributed to the creation of Virtutes Occultae, the composer makes comparisons between his process and the use of generative commercial music software to create music, in order to draw distinctions between creating computer-generated music to extend aesthetic sensibilities and creating computer-generated music that iterates based on established commercial styles. Finally, the composer proposes future paths for further investigation involving the development of new musical styles through computer-generated music and reactive computer improvisation.
A Study on Gaze Control - Game Accessibility Among Novice Players and Motor Disabled People

Gaze control is a substitution for disabled people to play computer games. However, many disabled people may be inexperienced in games and/or novices using gaze-control. This study presents a game accessibility approach using gaze control modality for novice players and disabled people. A workshop was conducted involving a playtest on three games with gaze-control. The game experiences were observed, recorded, and evaluated with mixed methods. The study estimated the gaze control game accessibility by System Usability Scale (SUS), Game Experience Questionnaire (GEQ), and an open-ended questionnaire. The gaze control modality demonstrated possible game accessibility to people with motor disabilities. The results also indicate that the challenge of game mechanics and the accuracy of the gaze-control system are two significant impact factors. Further research will be conducted on gaze-control games including more disabled people, and also develop the data analysis methods for evaluating gaze-control modality for game accessibility.
Video-Surveillance Tools for Monitoring Social Responsibility Under Covid-19 Restrictions

The paper presents a number of recently developed methods for automatic categorization of socio-cognitive crowd behavior from video surveillance data. Sadly and unexpectedly, the recent pandemic outbreak of Covid-19 created a new important niche for such tasks, which otherwise have been rather associated with police monitoring public events or oppressive regimes tightly controlling selected communities. First, we argue that a recently proposed (see [31]) general socio-cognitive categorization of crowd behavior well corresponds to the needs of social distancing monitoring. It is explained how each of four proposed categories represents a different level of social (ir)responsibility in public spaces. Then, several techniques are presented which can be used to perform in real time such a categorization, based only the raw-data inputs (i.e. video-sequences from surveillance cameras). In particular, we discuss: (a) selected detection and tracking aspects for individual people and their groups, (b) practicality of data association combining results of detection and tracking, and (c) mid-level features proposed for neural-network-based classifiers of the behavior categories. Some illustrative results obtained in the developed feasibility studies are also included.
Single-Shot Object Detection with Split and Combine Blocks

Feature fusion is widely used in various neural network-based visual recognition tasks, such as object detection, to enhance the quality of feature representation. It is common practice for both the one-stage object detectors and the two-stage object detectors to implement feature fusion in feature pyramid networks (FPN) to enhance the capacity to detect objects of different scales. In this work, we propose a novel and efficient feature fusion unit, which is referred to as the Split and Combine (SC) Block, that splits the input feature maps into several parts, then processes these sub-feature maps with different emphasis, and finally gradually concatenates the outputs one-by-one. The SC block implicitly encourages the network to focus on features that are more important to the task, thus improving network efficiency and reducing inference computations. In order to prove our analysis and conclusions, a backbone network and an FPN employing this technique are assembled into a one-stage detector and evaluated on the MS COCO dataset. With the newly introduced SC block and other novel training tricks, our detector achieves a good speed-accuracy trade-off on COCO test-dev set, with 37.1% AP (average precision) at 51 FPS and 38.9% AP at 40 FPS.
Knowledge Graph for China’s Genealogy

Genealogical knowledge graphs depict the relationships of family networks and the development of family histories. They can help researchers to analyze and understand genealogical data, search for genealogical roots, and explore the origins of a family more easily. However, the multi-type, multisource dynamic changes and specialized nature of genealogical data bring challenges to the development of contemporary knowledge graph models. Applying existing methods to genealogical data can result in problems of overlooking certain specialized vocabulary and dynamic properties such as personal experiences. In this paper, we propose a genealogical knowledge graph model GKGM that combines HAO intelligence (h uman intelligence + a rtificial intelligence + o rganizational intelligence) and ontology granularity division technology to address the above problems. Furthermore, a method of applying the model to construct genealogical knowledge graphs is demonstrated, and an experiment conducted on a real-world genealogical dataset verifies the feasibility and effectiveness of the model.
Performance Comparison of Data Classification based on Modern Convolutional Neural Network Architectures

In recent years, convolutional neural network (CNN) has become the most powerful tool to deal with some computer vision applications, for example, classification, object detection, semantic segmentation, and so on. This benefits from the development of computer hardware and theory of deep learning. Many state-of-the-art CNN architectures have been proposed and they produce better performance than previous conventional methods. However, the effects of these modern CNN architectures are usually demonstrated based on some public datasets. The performance of these architectures on unpublished datasets is still not clear. To solve this problem, we compare classification performance of six different CNN architectures based on our own Lead isotope dataset. Six architectures are ZFNet, VGG16, ResNet18, ResNet101, GoogLeNet, DenseNet, respectively. Through experiments, we find that all CNN architectures can produce satisfactory classification results, and the performance of some architectures in our dataset is different from that in some public datasets.
A Semiautomatic Approach to Identify Architectural Technical Debt from Heterogeneous Artifacts

Architectural Technical Debt (ATD) is a metaphor used to describe decisions taken by software architects to accomplish short-term goals but possibly negatively affecting the long-term health of the system. However, ATD doesn’t receive enough attention for the architect teams because it is hard to identify, to measure, to prioritize, and its value is related to long-term maintenance and evolution of a system. In this research, we propose a model-driven approach that focuses on building a binary classification model for ATD identification based on the information gathered from artifacts produced during architecture design. This model will allow software architects to support the managing of conscious and unconscious ATD in their software projects. This proposal focuses on TD at the architecture-level only without considering source code. The effectiveness of this proposal will be evaluated using case studies and expert interviews.
A Probabilistic Label Association Algorithm for Distributed Labeled Multi-Bernoulli Filtering

We consider a distributed labeled multi-Bernoulli (LMB) filter that uses the generalized covariance intersection technique for fusing the local LMB distributions. A critical aspect of such filters is to correctly associate labeled Bernoulli components describing the same object at different sensors. Here, we improve on previously proposed association schemes by introducing a probabilistic framework and algorithm for object (label) association. Instead of enforcing a hard association, we propose to compute association probabilities and use them in the fusion of the LMB posterior distributions. To develop our probabilistic label association scheme, we first derive a formulation of the fused multiobject distribution that involves a label association distribution. We then show that approximating the label association distribution by the product of its marginals results in a fused multiobject distribution that is again of LMB type. An efficient LMB fusion algorithm is finally obtained by using a belief propagation scheme for fast approximate marginalization and a Gaussian approximation. Simulation results demonstrate that the resulting distributed LMB filter outperforms a state-of-the-art method using hard label association.
Fuzzy partitioning of clinical data for DMT2 patients

The present study represents an original approach to data interpretation of clinical data for patients with diagnosis diabetes mellitus type 2 (DMT2) using fuzzy clustering as a tool for intelligent data analysis. Fuzzy clustering is often used in classification and interpretation of medical data (including in medical diagnosis studies) but in this study it is applied with a different goal: to separate a group of 100 patients with DMT2 from a control group of healthy volunteers and, further, to reveal three different patterns of similarity between the patients. Each pattern is described by specific descriptors (variables), which ensure pattern interpretation by appearance of underling disease to DMT2.
Hierarchical One-out-of-Many Proofs With Applications to Blockchain Privacy and Ring Signatures

The one-out-of-many proof is a cryptographic zero-knowledge construction enabling the prover to demonstrate knowledge of a secret element among the given public list of cryptographic commitments opening to zero. This method is relying on standard Decisional Diffie-Hellman security assumptions and can result in efficient accountable ring signature schemes [4] and proofs of set memberships [5] with a signature size smaller than all existing alternative schemes relying on standard assumptions. This construction also serves as a fundamental building block for numerous recent blockchain privacy protocols including Anonymous Zether [1], [2], Zerocoin [3], Lelantus [11], Lelantus-MW [9], Triptych [14] and Triptych-2 [15]. In this work, we introduce a new method of instantiating one-out-of-many proofs which reduces the proof generation time by an order of magnitude. Our approach still results in shorter proofs comprised of only a logarithmic number of commitments and does not compromise the highly efficient batch verification properties endemic to the original construction.
Meta Learning for Few-Shot One-Class Classification

We propose a method that can perform one-class classification given only a small number of examples from the target class and none from the others. We formulate the learning of meaningful features for one-class classification as a meta-learning problem in which the meta-training stage repeatedly simulates one-class classification, using the classification loss of the chosen algorithm to learn a feature representation. To learn these representations, we require only multiclass data from similar tasks. We show how the Support Vector Data Description method can be used with our method, and also propose a simpler variant based on Prototypical Networks that obtains comparable performance, indicating that learning feature representations directly from data may be more important than which one-class algorithm we choose. We validate our approach by adapting few-shot classification datasets to the few-shot one-class classification scenario, obtaining similar results to the state-of-the-art of traditional one-class classification, and that improves upon that of one-class classification baselines employed in the few-shot setting.
Latent Feature Modelling for Recommender Systems

Matrix factorization is one of the most successful model-based collaborative filtering approaches in recommender systems. Nevertheless, useful latent user features can lead to a more accurate recommendation. However, user privacy and cross-domains access restrictions challenge collection and analysis of such information. In this study, we propose a feature extraction method (WAFE) which leverages user-item interaction history to extract useful latent user features. We also propose a rating prediction approach that incorporates the local mean of users' and items' ratings. We evaluate our proposed model using two real-world benchmark datasets and compare its performance against the state-of-the-art matrix factorization collaborative filtering methods. Evaluation results show that proposed method outperforms the existing methods.
SSKD: Self-Supervised Knowledge Distillation for Cross Domain Adaptive Person Re-Identification

Domain adaptive person re-identification (re-ID) is a challenging task due to the large discrepancy between the source domain and the target domain. To reduce the domain discrepancy, existing methods mainly attempt to generate pseudo labels for unlabeled target images by clustering algorithms. However, clustering methods tend to bring noisy labels and the rich fine-grained details in unlabeled images are not sufficiently exploited. In this paper, we seek to improve the quality of labels by capturing feature representation from multiple augmented views of unlabeled images. To this end, we propose a Self-Supervised Knowledge Distillation (SSKD) technique containing two modules, the identity learning and the soft label learning. Identity learning explores the relationship between unlabeled samples and predicts their one-hot labels by clustering to give exact information for confidently distinguished images. Soft label learning regards labels as a distribution and induces an image to be associated with several related classes for training peer network in a self-supervised manner, where the slowly evolving network is a core to obtain soft labels as a gentle constraint for reliable images. Finally, the two modules can resist label noise for re-ID by enhancing each other and systematically integrating label information from unlabeled images. Extensive experiments on several adaptation tasks demonstrate that the proposed method outperforms the current state-of-the-art approaches by large margins.
Video Steganography using Curvelet Transform and Elliptic Curve Cryptography

Video steganography mainly deals with secret data transmission in a carrier video file without being visually noticeable by intruders. Video steganography is preferred over image steganography because a video carries more space in comparison to an image. The main concept of information hiding consists of a cover media, which is a greyscale or a color video, a secret data, which is an image or text, and a stego key. Here a secure video steganography method has been proposed which uses Curvelet Transform for secret data embedding, Elliptic Curve Cryptography for stego key encryption and a threshold algorithm for the determination of the amount of secret data to be encoded per frame. A video is a collection of various frames. The frames are selected randomly from the cover video and the frame number of the respective frames has been indexed in the stego key to find the secret data embedding location. Here, the selection of frames in a sequential manner has been avoided to improve security. For enhanced security, the stego key is also encrypted using Elliptic Curve Integrated Encryption Scheme (ECIES). Fast Discrete Curvelet Transform (FDCT) has been applied to the frames of the cover video and the curvelet coefficients have been modified to obscure the secret data to produce the stego video.
DeepReturn: A deep neural network can learn how to detect previously-unseen ROP payloads without using any heuristics

Return-oriented programming (ROP) is a code reuse attack that chains short snippets of existing code to perform arbitrary operations on target machines. Existing detection methods against ROP exhibit unsatisfactory detection accuracy and/or have high runtime overhead. In this paper, we present DeepReturn, which innovatively combines address space layout guided disassembly and deep neural networks to detect ROP payloads. The disassembler treats application input data as code pointers and aims to find any potential gadget chains, which are then classified by a deep neural network as benign or malicious. Our experiments show that DeepReturn has high detection rate (99.3%) and a very low false positive rate (0.01%). DeepReturn successfully detects all of the 100 real-world ROP exploits that are collected in-the-wild, created manually or created by ROP exploit generation tools. DeepReturn is non-intrusive and does not incur any runtime overhead to the protected program.
A Computer Vision-Based System for a Tangram Game in a Social Robot

Social Robotics is currently gaining ground as it allows robots to have an emphatic behaviour toward humans which facilitates the creation of emotional bonds with them. In this context, robots are starting to support caregivers in stimulation exercises, such as cognitive stimulation therapy, for elderly people. This work focuses on endowing the social robot Mini with perception capabilities to detect a classic game, the Tangram. Besides, the robot is able to follow the game mechanics and, using its interaction capabilities, leads the game and encourage users to complete the game.
TopicsRanksDC: Distance-Based Topic Ranking Applied on Two-Class Data

In this paper, we introduce a novel approach named TopicsRanksDC for topics ranking based on the distance between two clusters that are generated by each topic. We assume that our data consists of text documents that are associated with two-classes. Our approach ranks each topic contained in these text documents by its significance for separating the two-classes. Firstly, the algorithm detects topics using Latent Dirichlet Allocation (LDA). The words defining each topic are represented as two clusters, where each one is associated with one of the classes. We compute four distance metrics, Single Linkage, Complete Linkage, Average Linkage and distance between the centroid. We compare the results of LDA topics and random topics. The results show that the rank for LDA topics is much higher than random topics. The results of TopicsRanksDC tool are promising for future work to enable search engines to suggest related topics.
An Attention Model for Mashup Tag Recommendation

Mashups have emerged as a popular technique to compose value-added web services/APIs, to fulfill some complicated business needs. This has increased the number of available mashups over the internet. The increase however, poses a new requirement of organizing and managing these mashups for better understanding and discovery. For this reason, tags have become highly important because they describe items and allows for easy discovery. Most existing tag recommendation methods typically follow a manual process based on controlled vocabulary, or consider tags as words in isolation contained in mashup descriptions. Such methods therefore fail to characterize the diverse functional features of mashups. This work proposes an attention model to automatically recommend mashup tags. Specifically, our proposed model has two levels of attention mechanisms applied at the word- and sentence-levels and subsequently recommend top-N words with highest attention weights as tags. Our model is based on the intuition that not every word in a mashup description is equally relevant in identifying its functional aspects. Therefore, determining the relevant sections involves modeling the interactions of the words, not just their presence in isolation. We demonstrate the effectiveness of our method by conducting extensive experiments on a real-world dataset crawled from www.programmableweb.com . We also compare our method with some baseline tag recommendation methods for verification.
Constructing a Simple Verbal Compiler

The paper's purpose is to design and program the four operation-calculators that receives voice instructions and runs them as either a voice or text phase.The Calculator simulates the work of the Compiler.The paper is a practical example programmed to support that it is possible to construct a verbal Compiler.
On the boomerang uniformity of permutations of low Carlitz rank

Finding permutation polynomials with low differential and boomerang uniformity is an important topic in S-box designs of many block ciphers. For example, AES chooses the inverse function as its S-box, which is differentially 4-uniform and boomerang 6-uniform. Also there has been considerable research on many non-quadratic permutations which are modifications of the inverse function. In this paper, we give a novel approach which shows that plenty of existing modifications of the inverse function are in fact affine equivalent to permutations of low Carlitz rank, and those modifications cannot be APN. We also present the complete list of permutations of Carlitz rank 3 having the boomerang uniformity six, and give the complete classification of the differential uniformities of permutations of Carlitz rank 3. As an application, we provide all the involutions of Carlitz rank 3 having the boomerang uniformity six.
The Stein effect for Fréchet means

The Fréchet mean is a useful description of location for a probability distribution on a metric space that is not necessarily a vector space. This article considers simultaneous estimation of multiple Fréchet means from a decision-theoretic perspective, and in particular, the extent to which the unbiased estimator of a Fréchet mean can be dominated by a generalization of the James–Stein shrinkage estimator. It is shown that if the metric space satisfies a nonpositive curvature condition, then this generalized James–Stein estimator asymptotically dominates the unbiased estimator as the dimension of the space grows. These results hold for a large class of distributions on a variety of spaces, including Hilbert spaces and, therefore, partially extend known results on the applicability of the James–Stein estimator to nonnormal distributions on Euclidean spaces. Simulation studies on phylogenetic trees and symmetric positive definite matrices are presented, numerically demonstrating the efficacy of this generalized James–Stein estimator.