Reviewerly/exHarmony huggingface dataset:
An Improved Network for Small Object Detection Based on YOLOv4-Tiny-3L

YOLOv4-Tiny-3L is an object detection network that is very suitable deployed on low-end GPU devices, edge computation devices and embedded devices. But the tiny model is speed oriented. Spatial Pyramid Pooling is a method for improving receptive fields of network without significantly computational cost increasing. In this paper, an improved neural network structure based on YOLOv4-Tiny-3L and SPP is proposed. On MS COCO dataset, our proposed network achieves higher accuracy than YOLOv4-Tiny and YOLOv4-Tiny-3L and achieves 22.1% AP (43.3% \(A{P}_{50}\)). Especially, the accuracy in small objects detection increase by 4.9% and 5.1% compared with YOLOv4-Tiny-3L and YOLOv4-Tiny separately. In the end, we take two life scenes photo to verify for actual result comparison. The results show that our proposed network has better recall and accuracy.
Passive Object-based Video Authentication Using Stereo Statistical Descriptor on Wavelet Decomposition

Determining video authenticity has become a challenging task in video forgery detection and localization systems. One of the core difficulties is to extract a suitable feature descriptor. The recent studies on passive object-based video authentication are limited to localize the forged frames in copy-paste/delete object forgery. That is done without considering the object motion interpolation. It performs video authentication on simple videos with a static camera without using dynamic texture. This research work proposes an Action Passive Object-based Video Authentication Algorithm (APOVA). The APOVA consists a new statistical texture descriptor and a forged keyframes localization algorithm to validate video data. The APOVA algorithm has been tested on the Shih-Tang dataset. The experimental results shows that APOVA can obtain the best results in accuracy using the proposed descriptor. In addition, it is able to achieve the best performance in forged keyframes localization with F0.5_score = 0.93 and video authentication with F0.5_score = 0.99. As a result, APOVA based on APOKL is considered suitable algorithm for complex color video authentication, especially for passive object-based video forgery systems.
Identification Method of Ancient Ceramics Revision

Along with the promotion of economics and cultural, more and more people adore collecting some arts, for example, ancient or modern ceramic and so on. Ancient ceramic has significant research and collectible value of its historical and humanistic value. It only presents some subtle changes on shape and rare surfaces in comparison with others. The key problem of the identification of ancient ceramics is a conflict between the high-performance requests and the low accuracy and precise of algorithm. The paper proposes a novel identification method of ancient ceramics based on VGG16-GSSVM with few-shot. It uses the pre-trained VGG16 to extract the shape features of ancient ceramics, and trains a SVM model to be the classifier of VGG16. It applies Grid Search (GS: Grid Search) algorithm to determine its optimal parameters of SVM. Finally, the paper uses VGG16-GSSVM to identify pottery. The experiment shows that VGG16-GSSVM model has a higher accuracy and efficiency than any of CNN and CNN-SVM model while the ratio of the training to test is 1:3.
An Application of Evaluation of Human Sketches using Deep Learning Technique

This research is a study of the evaluation of full-body sketches and the principle of the human pose estimation using the OpenPose library, a method to detect 18 keypoints on a human structure. The dataset used in this research was drawing sketches of 22 first-year students, each of whom drew three drawings of three models. Detected keypoints are calculated to determine the angle and distance between keypoints, which provides 26 features. These features were modeled using ANN for predicting the grades of drawings classified as good, moderate, poor. The resulting keypoints are then taken to find the angles and distances of the skeleton, extracting 26 features and taking these features to create a model using ANN classification. The performance of the model was evaluated using with 56% accuracy
How Are Deepfake Videos Detected? An Initial User Study

This paper describes an exploratory study of deepfake video detection from a user’s perspective. Through semi-structured interviews, participants were asked to identify real and deepfake videos, and explain how they arrived at their conclusions. From the interviews, two sets of features were derived. One was associated with correct deepfake identification while the other was associated with incorrect identification. Interestingly, the two sets had overlapping features suggesting the difficulties associated with deepfake identification. Further, the majority of participants could not correctly identify all their assigned videos.
Hierarchical Clustering of Spectral Images with Spatial Constraints for the Rapid Processing of Large and Heterogeneous Data Sets

When dealing with full spectrum images in which each pixel is characterized by a full spectrum, i.e. spectral images, standard segmentation methods, such as k-means or hierarchical clustering might be either inapplicable or inappropriate ; one aspect being the multi-GB size of such data set leading to very expensive computations. In the present contribution, we propose an approach to spectral image segmentation combining hierarchical clustering and spatial constraints. On the one hand spatial constraints allow to implement an algorithm with a reasonable computation time to obtain a segmentation and with a certain level of robustness with respect to the signal-to-noise ratio since the prior knowledge injected by the spatial constraint partially compensates for the increase in noise level. On the other hand hierarchical clustering provides a statistically sound and known framework that allows accurate reporting of the instrument noise model. In terms of applications, this segmentation problem is encountered particularly in the study of ancient materials that benefits from the wealth of information provided by the acquisition of spectral images. In the last few years, data collection has been considerably accelerated, enabling the characterization of the sample with a high dynamic range in both the spatial dimensions and composition and leading to an average size of a single data set in the tens of GB range. Hence we also considered computational and memory complexity when developing the herein proposed algorithm. Taking on this application domain, we illustrate the proposed algorithm on a X-ray fluorescence spectral image collected on an ca. 100 Myr fossil fish, as well as on simulated data to assess the sensitivity of the results to the noise level. For such experiment, the lower sensitivity to noise simultaneously lead to an increase in the spatial definition of the collected spectral image, thanks to the faster acquisition time, and to a reduction in the potentially harmful radiation dose density to which the samples are subjected.
Moving Walls and Talking Floors: Design Considerations on Magical Media Architecture Experiences

An ocular-centric fixation governs the majority of examples in both traditional and media architecture. Stepping away from this predominance, the three technology-augmented installations described in this contribution focus primarily on multi-sensory spatial interactions. To trigger curiosity and establish engagement, we make use of magic as a metaphor to encourage playful exploration of the installations’ possibilities and speculation on their modus-operandi enigmas. This combination elevates the exhibition space as it appoints the human body as the key constituent of the experience. Placed at the entrance hall of an integration school (for Deaf, hard-of-hearing and hearing children and staff), our installations are aimed at raising population awareness by highlighting this population’s particularities. In this paper, we detail the constellation of magical design characteristics of our artifacts and propose a set of considerations that could bring forth magical media architecture experiences.
Lattice-Based Logarithmic-Size Non-Interactive Deniable Ring Signatures

Deniable ring signature can be regarded as group signature without group manager, in which a singer is capable of singing a message anonymously, but, if necessary, each ring member is allowed to confirm or disavowal its involvement in the signature via an interactive mechanism between the ring member and the verifier. This attractive feature makes the deniable ring signature find many applications in the real world. In this work, we propose an efficient scheme with signature size logarithmic to the cardinality of the ring. From a high level, we adapt Libert et al.’s zero-knowledge argument system (Eurocrypt 2016) to allow the prover to convince the verifier that its witness satisfies an additional condition. Then, using the Fait-Shamir transformation, we get a non-interactive deniable ring signature scheme that satisfies the anonymity, traceability, and non-frameability under the small integer solution assumption in the random oracle model.
GRANEF: Utilization of a Graph Database for Network Forensics

Understanding the information in captured network traffic, extracting the necessary data, and performing incident investigations are principal tasks of network forensics. The analysis of such data is typically performed by tools allowing manual browsing, filtering, and aggregation or tools based on statistical analyses and visualizations facilitating data comprehension. However, the human brain is used to perceiving the data in associations, which these tools can provide only in a limited form. We introduce a GRANEF toolkit that demonstrates a new approach to exploratory network data analysis based on associations stored in a graph database. In this article, we describe data transformation principles, utilization of a scalable graph database, and data analysis techniques. We then discuss and evaluate our proposed approach using a realistic dataset. Although we are at the beginning of our research, the current results show the great potential of association-based analysis.
Framework for Location Based Attendance System by Using Fourth Industrial Revolution (4IR) Technologies

Information Technology (IT) is an emerging field that plays a significant role in every field of today’s world. In the education systems, IT has replaced many traditional methods with trending digital technologies and solved the problems and hurdles that usually occur. Besides these, IT is a key factor that is responsible for digitization and automation that ultimately have led us to the 4th industrial revolution (4IR), which is an upgrade of the previous 3 industrial revolutions. The 4IR is not only for industrial production, but it is also involved in almost every field including education and commercial domains. In every organization, including an educational institute or any other commercial organization, attendance has a great importance. In the recent past, several IT technologies have been used to replace the traditional paper-based attendance method with digital attendance mode, for that purpose, there have been several techniques in use since long time i.e. Near Field Communication; NFC, Radio Frequency Identification; RFID, Biometric, Quick Response Code; QR-Code, etc. Though, these types of techniques were very efficient but had some flaws such as risks of prone attendance, the queue of a crowd waiting in front of attendance device, high time consuming etc. This research was planned to overcome these issues by introducing a new approach by designing a framework for location-based attendance system using digital technologies of the 4IR. The designed framework was based on BYOD (Bring Your Own Device) trend, in which students rather than teachers can mark their attendance using their own devices. This framework was a 3 layered framework i.e. client layer, data synchronization layer, and server layer. The framework was validated by developing a system and testing it at the Information Technology Centre, Sindh Agriculture University Tandojam, Pakistan. For calculating the empirical results, an evaluation was performed. The designed framework proved to be more secure, all the data in the framework was encrypted with a strong encryption algorithm, final attendance of successful students was stored in the blockchain structure to maintain the confidentiality and integrity in data. The designed framework used triple authentication i.e. biometric authentication, QR-code authentication, and location based authentication that reduced the risk of prone attendance.
Delegated Authorization Framework for EHR Services using Attribute Based Encryption

Medical organizations find it challenging to adopt cloud-based Electronic Health Records (EHR) services due to the risk of data breaches and the resulting compromise of patient data. Existing authorization models follow a patient-centric approach for EHR management, where the responsibility of authorizing data access is handled at the patients’ end. This creates significant overhead for the patient, who must authorize every access of their health record. It is also not practical given that multiple personnel are typically involved in providing care and that the patient may not always be in a state to provide this authorization.
On assigning probabilities to new hypotheses

The paper proposes the way how to assign a proper prior probability to a new, generally compound, hypothesis. To this purpose, it uses the minimum relative-entropy principle and a forecaster-based knowledge transfer. Methodologically, it opens a way towards enriching the standard Bayesian framework by the possibility to extend the set of models during learning without the need to restart. The presented use scenarios concern: (a) creating new hypotheses, (b) learning problems with an insufficient amount of data, and (c) sequential Monte Carlo estimation. They indicate a strong application potential of the proposed technique. Related interesting open research problems are listed.
Multi-party Private Set Operations with an External Decider

A Private Set Operation (PSO) protocol involves at least two parties with their private input sets. The goal of the protocol is to learn the output of a set operation, e.g., set intersection, on the parties’ input sets, without revealing any information about the items that are not in the output set. Commonly, the outcome of the set operation is revealed to parties and no one else. However, in many application areas of PSO, the result of the set operation should be learned by an external participant who does not have an input set. We call this participant the decider. In this paper, we present new variants of multi-party PSO, for the external decider setting. All parties except the decider have a private set. Parties other than the decider neither learn this result, nor anything else from this protocol. Moreover, we studied generic solutions to the problem of PSO in the presence of an external decider.
Analysis of the Features of the Introduction of Distance Learning During the Coronavirus Pandemic

The article devoted to distance education. The mail task of the article is to justify and explain different aspekt of distance learning. The article shows forms of learning, the major directions of development of distance learning, the differences between distance learning and the traditional one. The problems that students and teachers face in distance learning are distinguished. Distance education is proved to be regarded as an independent form of training, because it has significant distinctions, which can not be realised in the traditional manner.
A Sample-Efficiency Comparison Between Evolutionary Algorithms and Deep Reinforcement Learning for Path Planning in an Environmental Patrolling Mission

For water environmental monitoring tasks, the use of Autonomous Surface Vehicles has been a very common option to substitute human interaction and increase the efficiency and speed in the water quality measuring process. This task requires an optimization of the trajectories of the vehicle following a non-homogeneous interest coverage criterion which is a hard optimization problem. This issue is aggravated whenever the resolution of the water resource to be monitored scales up. Since two of the preferred approaches for path planning of autonomous vehicles in the literature are Evolutionary Algorithms and Reinforcement Learning, in this paper, We compare the performance of both techniques in a simulator of Ypacarai Lake in Asunción (Paraguay). The results show that the evolutionary approach is 50% more efficient for the lowest resolution but scales badly. Regarding the learning stability and sparsity of the trajectory optimality, the Double Deep Q-Learning algorithm has better convergence, but it appears to be less robust than the evolutionary approach. Finally, in a generalization analysis, the Deep Learning approach proves to be 35% more effective in reacting to scenario changes.
Explanation in Constraint Satisfaction: A Survey

Much of the focus on explanation in the field of artificial intelligence has focused on machine learning methods and, in particular, concepts produced by advanced methods such as neural networks and deep learning. However, there has been a long history of explanation generation in the general field of constraint satisfaction, one of the AI's most ubiquitous subfields. In this paper we survey the major seminal papers on the explanation and constraints, as well as some more recent works. The survey sets out to unify many disparate lines of work in areas such as model-based diagnosis, constraint programming, Boolean satisfiability, truth maintenance systems, quantified logics, and related areas.
Multiple-wave lateral shearing interferometry with a liquid-crystal geometric phase grating

A multiple-wave lateral shearing interferometer extends upon the traditional lateral shearing interferometry by producing multiple sheared copies of the incoming light. By using a special grating instead of a shear plate, it is able to produce fringes in multiple directions at the same time. This makes it possible to do single-shot reconstruction of both phase and amplitude aberrations. Instead of a surface relief grating, we propose to use a patterned half-wave plate manufactured using direct-write techniques that acts as a liquid-crystal geometric phase grating. We demonstrate its wavefront sensing capabilities with laboratory measurements with an ALPAO-97 deformable mirror in monochromatic light. Finally, we present on-sky measurements performed at the William Herschel Telescope, showing broadband operation in unpolarized light.
On the density for sums of independent Mittag-Leffler variates with common order

This note demonstrates a convenient approach to finding the density for the sum of independent Mittag-Leffler distributed random variables when they share a common order. The approach uses a well-known integral relation of the Mittag-Leffler function which lends itself to a divided difference interpretation for the convolution of such functions.
Identifying communicative functions in discourse with content types

Abstract Texts are not monolithic entities but rather coherent collections of micro illocutionary acts which help to convey a unitary message of content and purpose. Identifying such text segments is challenging because they require a fine-grained level of analysis even within a single sentence. At the same time, accessing them facilitates the analysis of the communicative functions of a text as well as the identification of relevant information. We propose an empirical framework for modelling micro illocutionary acts at clause level, that we call content types, grounded on linguistic theories of text types, in particular on the framework proposed by Werlich in 1976. We make available a newly annotated corpus of 279 documents (for a total of more than 180,000 tokens) belonging to different genres and temporal periods, based on a dedicated annotation scheme. We obtain an average Cohen’s kappa of 0.89 at token level. We achieve an average F1 score of 74.99% on the automatic classification of content types using a bi-LSTM model. Similar results are obtained on contemporary and historical documents, while performances on genres are more varied. This work promotes a discourse-oriented approach to information extraction and cross-fertilisation across disciplines through a computationally-aided linguistic analysis.
UAV Object Tracking Application Based on Patch Color Group Feature on Embedded System

The discriminative object tracking system for unmanned aerial vehicles (UAVs) is widely used in numerous applications. While an ample amount of research has been carried out in this domain, implementing a low computational cost algorithm on a UAV onboard embedded system is still challenging. To address this issue, we propose a low computational complexity discriminative object tracking system for UAVs approach using the patch color group feature (PCGF) framework in this work. The tracking object is separated into several non-overlapping local image patches then the features are extracted into the PCGFs, which consist of the Gaussian mixture model (GMM). The object location is calculated by the similar PCGFs comparison from the previous frame and current frame. The background PCGFs of the object are removed by four directions feature scanning and dynamic threshold comparison, which improve the performance accuracy. In the terms of speed execution, the proposed algorithm accomplished 32.5 frames per second (FPS) on the x64 CPU platform without a GPU accelerator and 17 FPS in Raspberry Pi 4. Therefore, this work could be considered as a good solution for achieving a low computational complexity PCGF algorithm on a UAV onboard embedded system to improve flight times.
Photonic arbitrary linear transformations in the frequency synthetic dimension

Arbitrary linear transformations are central to photonic quantum information processing and machine learning. We present a photonic architecture of time-modulated micro-ring resonators with tunable couplings between frequency modes to achieve arbitrary frequency-dimension linear transformations.
wCM based hybrid pre-processing algorithm for class imbalanced dataset

Imbalanced dataset classification is challenging because of the severely skewed class distribution. The traditional machine learning algorithms show degraded performance for these skewed datasets. However, there are additional characteristics of a classification dataset that are not only challenging for the traditional machine learning algorithms but also increase the difficulty when constructing a model for imbalanced datasets. Data complexity metrics identify these intrinsic characteristics, which cause substantial deterioration of the learning algorithms’ performance. Though many research efforts have been made to deal with class noise, none of them focused on imbalanced datasets coupled with other intrinsic factors. This paper presents a novel hybrid pre-processing algorithm focusing on treating the class-label noise in the imbalanced dataset, which suffers from other intrinsic factors such as class overlapping, non-linear class boundaries, small disjuncts, and borderline examples. This algorithm uses the wCM complexity metric (proposed for imbalanced dataset) to identify noisy, borderline, and other difficult instances of the dataset and then intelligently handles these instances. Experiments on synthetic datasets and real-world datasets with different levels of imbalance, noise, small disjuncts, class overlapping, and borderline examples are conducted to check the effectiveness of the proposed algorithm. The experimental results show that the proposed algorithm offers an interesting alternative to popular state-of-the-art pre-processing algorithms for effectively handling imbalanced datasets along with noise and other difficulties.