Reviewerly/exHarmony huggingface dataset:
Robust Image Watermarking Method in Wavelet Domain Based on SIFT Features

To protect the ownership of the digital content, the robustness of the watermarking algorithm is the most important metric to assess its affectiveness. However few state-of-the-art watermarking algorithms can resist the combinations of the conventional attacks such as jpeg compression and geometric transformation. In this paper, an improved robust image watermarking algorithm is thus proposed to address this issue. The watermark information is embedded in the low frequency domain of the wavelet transform by a quantization modulation method. When using watermark detection, use matching the position information of the SIFT key points is used to calculate the affine transformation parameters and the edge point parameters, and then inversely transform and reposition the detected image to recover the watermark synchronization information. Theoretical analysis and experimental results show that the proposed algorithm has high correlation accuracy and stable performance, and can effectively recover the watermark synchronization of watermark images subjected to rotation, scaling and translation attacks, so that the watermark algorithm can correctly detect or extract watermarks.
Offline Handwritten Chinese Character Recognition Based on Improved Googlenet

Aiming at the problem of misrecognition in offline handwritten Chinese character recognition, this paper proposed an improved shallow GoogLeNet and an error elimination algorithm. Compared with the shallow GoogLeNet, the improved shallow GoogLeNet not only reduced the number of training parameters, but also maintained the depth of the Inception structure. According to the error elimination algorithm, the confidence of the samples in the test results was calculated and the erroneous samples in the dataset were removed. Then the dataset was divided into multiple similar character sets and one dissimilar character set. When the recognition result was in the dissimilar character set, it can be used as the final result. Otherwise, the final result could be obtained by the secondary recognition on the corresponding similar character set. The training and testing of the experiment were carried out on the CISIA-HWDB1.1 dataset. The accuracy of the method was 97.48%, which was 6.68% higher than that of the GoogLeNet network.
Reconfigurable Hardware Architecture Design and Implementation for AI Deep Learning Accelerator

This paper proposes the Convolution Neural Network hardware accelerator architecture with 72PEs to achieve 14.4GOPS@100Mhz. The proposed CNN hardware accelerator is a layer-based architecture which can be reconfigured the layer parameters to suitable for different CNN architectures. The proposed architecture is based on operating three rows of input feature maps and then generate a one-row output feature map. The proposed architecture uses 322KB on-chip memory to store input feature maps, bias values, weights, and output feature maps to improve the efficiency of data reuse and reduce bandwidth utilization.
Information-Theoretic 2-Round MPC Without Round Collapsing: Adaptive Security, and More

We present simpler and improved constructions of 2-round protocols for secure multi-party computation (MPC) in the semi-honest setting. Our main results are new information-theoretically secure protocols for arithmetic NC1 in two settings: Our protocols achieve adaptive security and require only black-box access to the underlying field, whereas previous results only achieve static security and require non-black-box field access. Moreover, both results extend to polynomial-size circuits with computational and adaptive security, while relying on black-box access to a pseudorandom generator. In the OLE correlation model, the extended protocols for circuits tolerate up to $$n-1$$ corruptions. Along the way, we introduce a conceptually novel framework for 2-round MPC that does not rely on the round collapsing framework underlying all of the recent advances in 2-round MPC.
Toxic Language Detection in Social Media for Brazilian Portuguese: New Dataset and Multilingual Analysis.

Hate speech and toxic comments are a common concern of social media platform users. Although these comments are, fortunately, the minority in these platforms, they are still capable of causing harm. Therefore, identifying these comments is an important task for studying and preventing the proliferation of toxicity in social media. Previous work in automatically detecting toxic comments focus mainly in English, with very few work in languages like Brazilian Portuguese. In this paper, we propose a new large-scale dataset for Brazilian Portuguese with tweets annotated as either toxic or non-toxic or in different types of toxicity. We present our dataset collection and annotation process, where we aimed to select candidates covering multiple demographic groups. State-of-the-art BERT models were able to achieve 76% macro-F1 score using monolingual data in the binary case. We also show that large-scale monolingual data is still needed to create more accurate models, despite recent advances in multilingual approaches. An error analysis and experiments with multi-label classification show the difficulty of classifying certain types of toxic comments that appear less frequently in our data and highlights the need to develop models that are aware of different categories of toxicity.
Intra-Correlation Encoding for Chinese Sentence Intention Matching

Sentence intention matching is vital for natural language understanding.Especially for Chinese sentence intention matching task, due to the ambiguity of Chinese words, semantic missing or semantic confusion are more likely to occur in the encoding process.Although the existing methods have enriched text representation through pre-trained word embedding to solve this problem, due to the particularity of Chinese text, different granularities of pre-trained word embedding will affect the semantic description of a piece of text.In this paper, we propose an effective approach that combines charactergranularity and word-granularity features to perform sentence intention matching, and we utilize soft alignment attention to enhance the local information of sentences on the corresponding levels.The proposed method can capture sentence feature information from multiple perspectives and correlation information between different levels of sentences.By evaluating on BQ and LCQMC datasets, our model has achieved remarkable results, and demonstrates better or comparable performance with BERT-based models.
Enhancing discrete-modulated continuous-variable measurement-device-independent quantum key distribution via quantum catalysis

Abstract Discrete modulation can make up for the shortage of transmission distance in measurement-device-independent continuous-variable quantum key distribution (MDI-CVQKD), providing a unique advantage against all side-channel attacks but also creating a challenge for further performance improvement. Here we suggest a quantum catalysis (QC) approach for enhancing the performance of the discrete-modulated (DM) MDI-CVQKD in terms of the achievable secret key rate and lengthening the maximal transmission distance. The numerical simulation results show that the QC-based MDI-CVQKD with discrete modulation, involving a zero-photon catalysis (ZPC) operation, can not only obtain a higher secret key rate than the original DM protocol, but also contribute to a reasonable increase of the corresponding optimal variance. As for the extreme asymmetric and symmetric cases, the secret key rate and maximal transmission distance of the ZPC-involved DM MDI-CVQKD system can be further improved under the same parameters. This approach enables the system to tolerate lower reconciliation efficiency, which may provide excellent potential for practical implementations with state-of-art technology.
Mutual Information and Quantum Discord in Quantum State Discrimination with a Fixed Rate of Inconclusive Outcomes

We studied the mutual information and quantum discord that Alice and Bob share when Bob implements a discrimination with a fixed rate of inconclusive outcomes (FRIO) onto two pure non-orthogonal quantum states, generated with arbitrary a priori probabilities. FRIO discrimination interpolates between minimum error (ME) and unambiguous state discrimination (UD). ME and UD are well known discrimination protocols with several applications in quantum information theory. FRIO discrimination provides a more general framework where the discrimination process together with its applications can be studied. In this setting, we compared the performance of optimum probability of discrimination, mutual information, and quantum discord. We found that the accessible information is obtained when Bob implements the ME strategy. The most (least) efficient discrimination scheme is ME (UD), from the point of view of correlations that are lost in the initial state and remain in the final state, after Bob’s measurement.
ODIN: An Object Detection and Instance Segmentation Diagnosis Framework

Object detection and instance segmentation are major tasks in Computer Vision and have substantially progressed after the introduction of Deep Convolutional Neural Network (DCNN). Analyzing the performance of DCNNs is an open research issue, addressed with attention techniques that inspect the response of inner network layers to input stimuli. A complementary approach relies on the black-box diagnosis of errors, which exploits ad hoc metadata on the input data set and factors the performance into indicators sensible or impacted by specific facets of the input (e.g., object size, presence of occlusions, image acquisition conditions, etc.). In this paper we present an open source error diagnosis framework for object detection and instance segmentation that helps model developers to add meta-annotations to their data sets, to compute performance metrics split by meta-annotation values, and to visualize diagnosis reports. The framework accepts the popular PASCAL VOC and MS COCO input formats, is agnostic to the training platform, and can be extended with application- and domain-specific meta-annotations and metrics with almost no coding.
Empirical evaluation of user experience using lean product and process development: A public institution case study in Indonesia

The convenience, speed, accuracy, security are the four main indicators of information quality that are concerning for people to be able access information in a public institution dashboard system. To find out whether the data displayed on the system is easily understood by the users, an empirical evaluation study might be indispensable to be performed. Evaluating user experience is one way to know whether a system is in accordance with user needs or not, whether related to data, interfaces, or system performance. This study assessed user experiences based on the product pyramid and its development process in the case of a public dashboard system in a public institution in Indonesia. The empirical experiment was conducted by 15 participants used a scenario that was deliberately designed and then gave answers to the single ease question (SEQ) and the system usability scale (SUS). The results show that SEQ measurement obtained almost 5.7 amount of score which indicates the application measurement results reach an easy level. The SUS results demonstrated that the system was in the acceptable category with a score around 81.3 Although the findings of the study might not contribute theoretically, it may demonstrate a practical consideration for the stakeholders, in terms of the next system development stage.
Fast Hough transform-based road markings detection for autonomous vehicle

The paper presents an algorithm for road markings detection in the image. The road markings are approximated by polyline with a restricted maximum curvature angle. To detect a marking segments an image is processed by a sliding window and for each window position, a straight line is detected by calculating Fast Hough Transform (FHT). Further, detected segments are grouped by relative position. Segments groups are then approximated by polylines. The algorithm was tested on real data collected from the front-looking camera of the autonomous vehicle driving at the experimental area “Kalibr” (Moscow). The road marking dataset used to evaluate the algorithm is publicly available at ftp://vis.iitp.ru/road markup dataset/. The precision of road markings detector was evaluated as 43%, and the recall as 73% which is sufficient for the autonomous vehicle precise positioning as demonstrated in [1].
Data modeling and evaluation of deep semantic annotation for cultural heritage images

Purpose To meet the emerging demand for fine-grained annotation and semantic enrichment of cultural heritage images, this paper proposes a new approach that can transcend the boundary of information organization theory and Panofsky's iconography theory. Design/methodology/approach After a systematic review of semantic data models for organizing cultural heritage images and a comparative analysis of the concept and characteristics of deep semantic annotation (DSA) and indexing, an integrated DSA framework for cultural heritage images as well as its principles and process was designed. Two experiments were conducted on two mural images from the Mogao Caves to evaluate the DSA framework's validity based on four criteria: depth, breadth, granularity and relation. Findings Results showed the proposed DSA framework included not only image metadata but also represented the storyline contained in the images by integrating domain terminology, ontology, thesaurus, taxonomy and natural language description into a multilevel structure. Originality/value DSA can reveal the aboutness, ofness and isness information contained within images, which can thus meet the demand for semantic enrichment and retrieval of cultural heritage images at a fine-grained level. This method can also help contribute to building a novel infrastructure for the increasing scholarship of digital humanities.
Combining Deep Learning and String Kernels for the Localization of Swiss German Tweets.

In this work, we introduce the methods proposed by the UnibucKernel team in solving the Social Media Variety Geolocation task featured in the 2020 VarDial Evaluation Campaign. We address only the second subtask, which targets a data set composed of nearly 30 thousand Swiss German Jodels. The dialect identification task is about accurately predicting the latitude and longitude of test samples. We frame the task as a double regression problem, employing a variety of machine learning approaches to predict both latitude and longitude. From simple models for regression, such as Support Vector Regression, to deep neural networks, such as Long Short-Term Memory networks and character-level convolutional neural networks, and, finally, to ensemble models based on meta-learners, such as XGBoost, our interest is focused on approaching the problem from a few different perspectives, in an attempt to minimize the prediction error. With the same goal in mind, we also considered many types of features, from high-level features, such as BERT embeddings, to low-level features, such as characters n-grams, which are known to provide good results in dialect identification. Our empirical results indicate that the handcrafted model based on string kernels outperforms the deep learning approaches. Nevertheless, our best performance is given by the ensemble model that combines both handcrafted and deep learning models.
Impact of Computational Power on Cryptography

From ancient times to modern digital era, one thing that never lost its importance is data. Extensive efforts have been made by people to protect and secure data. Advancements in computational technologies have provided many new ways to generate, process and utilize data, thus connecting technology with every sphere of human life. Adaptation of these new technologies has always been guaranteed and assured by the security procedures provided by cryptographic schemes. Same is true about multimedia which has emerged as one of the most demanding and exciting inventions of technological era. Currently, almost entire population of world is living within the reach of mobile network with about 4.57 billion active Internet users regularly generating, processing and sharing multimedia content. The only tool securing them digitally is the cryptographic techniques. The security of almost all of the cryptographic schemes is based on some computational and mathematical assumptions. Each new advancement in computational technology results in profound impact on cryptographic world by challenging these computational and mathematical assumptions. Recent computational advancements have compromised security of many famous and widely used cryptosystems like DES by favoring the cryptanalyst tools. Increasing computational power favors brute-force solutions, and attacks which were once considered computationally infeasible have become feasible. Emerging technologies like cloud computing, mobile cloud computing (MCC), quantum computing, parallel computing together with the advancements of GPUs, CUDA, clustering of standard CPUs and FPGA-based computing have provided new ways to solve many problems like integer factorization problem (IFP), discrete logarithm problem (DLP), elliptic curve discrete logarithm problem (ECDLP) etc., which were previously considered intractable. This has also resulted in great impact on many cryptosystems. Utilization of growing computational power has also resulted in development of robust and secure cryptosystems. The time taken for encryption and decryption especially in case of voluminous multimedia data has considerably reduced due to these computational advancements. Some security schemes like the blockchain have been able to achieve extraordinary security by utilizing the computational power of the miners. In this chapter, we study and analyze the impact of computational power on cryptography. From classical cryptography to modern cryptography, we study how security of various cryptosystems has changed with the advancements in computational power. We also study the impact of quantum computing on some widely used cryptosystems.
Analysis of implemented part of speech tagger approaches: The case of Ethiopian languages

Objective: To review Part of Speech (POS) tagging works that have been done for the Ethiopian languages.Methods: All methods that have been implemented to develop POS tagging for the Ethiopian languages have been mentioned.Findings: Since all implemented POS tagging methods have been mentioned in this work, the result will be used for future natural language processing researchers to select the best methodology.Novelty: The work includes all implemented POS tagging research works for the Ethiopian languages.
Selection and Spatial Layout of Urban Sports Facilities Guided by Residents' Demand

In order to explore the theory of the spatial layout of urban sports facilities, starting with the analysis of theoretical knowledge, the current situation of public sports facilities in the central urban area of Jinan is analyzed, the various factors affecting the planning layout are discussed, and the strategies and methods of the layout planning of public sports facilities in Jinan are summarized. The results show that the layout planning of public sports facilities should follow the corresponding patterns and principles. The layout of public sports facilities at all levels should fully consider the factors of urban public transport, urban management system, urban public functions, and reasonable service radius of public sports facilities. It can be seen that excessive pursuit of efficiency will lead to excessive service radius and poor accessibility of urban public sports facilities; excessive pursuit of fairness will result in a small and scattered layout pattern, which easily leads to idle waste.
Meta-learning Symmetries by Reparameterization

Many successful deep learning architectures are equivariant to certain transformations in order to conserve parameters and improve generalization: most famously, convolution layers are equivariant to shifts of the input. This approach only works when practitioners know the symmetries of the task and can manually construct an architecture with the corresponding equivariances. Our goal is an approach for learning equivariances from data, without needing to design custom task-specific architectures. We present a method for learning and encoding equivariances into networks by learning corresponding parameter sharing patterns from data. Our method can provably represent equivariance-inducing parameter sharing for any finite group of symmetry transformations. Our experiments suggest that it can automatically learn to encode equivariances to common transformations used in image processing tasks.
WITHDRAWN: Breast cancers detection using deep learning algorithm

Deep learning method is the process of detection of breast cancer, it consist of many hidden layers to produce most appropriate outputs. This paper shows how to detect breast cancers at a very early stage using this algorithm that mostly uses computer vision, image processing, medical diagnosis and neural language processing. As like supervised learning method, it maps the input data-mammogram scanned image to a certain trained data to produce a proper result that classifies the output as benign or malignant type of tumor present in breast else it is normal. A cluster of 400 women were recruited for this research with proper ethical approval and consents from the concerned authorities. The study has succeeded in achieving its overall aim. Along with the main objective, this research has also differentiated the type of tumor that has been diagnosed.
A Gated Recurrent Unit Approach to Bitcoin Price Prediction

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An Empirical Analysis of the Python Package Index (PyPI)

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Wavelet Theory and Application in Communication and Signal Processing

Wavelet analysis is the recent development in applied mathematics. For several applications, Fourier analysis fails to provide tangible results due to non-stationary behavior of signals. In such situation, wavelet transforms can be used as a potential alternative. The book chapter starts with the description about importance of frequency domain representation with the concept of Fourier series and Fourier transform for periodic, aperiodic signals in continuous and discrete domain followed by shortcoming of Fourier transform. Further, Short Time Fourier Transform (STFT) will be discussed to induce the concept of time frequency analysis. Explanation of Continuous Wavelet Transform (CWT) and Discrete Wavelet Transform (DWT) will be provided with the help of theoretical approach involving mathematical equations. Decomposition of 1D and 2D signals will be discussed suitable examples, leading to application concept. Wavelet based communication systems are becoming popular due to growing multimedia applications. Wavelet based Orthogonal Frequency Division Multiplexing (OFDM) technique and its merit also presented. Biomedical signal processing is an emerging field where wavelet provides considerable improvement in performance ranging from extraction of abnormal areas and improved feature extraction scheme for further processing. Advancement in multimedia systems together with the developments in wireless technologies demands effective data compression schemes. Wavelet transform along with EZW, SPIHT algorithms are discussed. The chapter will be a useful guide to undergraduate and post graduate who would like to conduct a research study that include wavelet transform and its usage.