Reviewerly/exHarmony huggingface dataset:
Combined Classifier-Demodulator Scheme Based on LSTM Architecture

When it comes to studies on smart receiver designs, using machine learning and deep learning techniques for the development of automatic modulation classifiers as well as demodulators which require little to no information about the transmitted signal or the channel state is an area of interest. Through this study, we have proposed a combined classifier-demodulator system that is entirely deep learning-based and one that is focused on higher-order quadrature amplitude modulation (QAM) schemes such as 64QAM and 256QAM that can be used in next-generation mobile technologies. The system was developed by training a bidirectional long-short-term memory (BiLSTM) and long-short-term memory (LSTM) network for the classifier and demodulator, respectively, using randomly generated data, demodulated using binary phase shift keying (BPSK), quadrature phase shift keying (QPSK), 16QAM, 64QAM, and 256QAM transmitted through a simulated additive white Gaussian noise (AWGN) channel of varying signal to noise ratio (SNR) levels. The classifier was then tested for its prediction accuracy while the demodulator models were tested against traditional mathematical models while calculating the effective capacity. The results showcased that the classifier worked extremely well for the QAM schemes across all SNR levels and less so with the PSK models. Considering the demodulator models’ performance, all schemes except the 256QAM demodulator were able to reach a zero or near zero bit error rate (BER) level within minimum acceptable SNR ranges.
Automation of Intent-based Service Operation with Models and AI/ML

To achieve digital transformation, it is becoming more important to flexibly and quickly provide information and communication technology (ICT) services according to various business requirements. Closed-loop control and its automation with artificial intelligence/machine learning (AI/ML) is a promising approach. However, most of such efforts focus on monitoring and analysis of resources. The challenges remain in automating operation of an entire service that consists of many resources in accordance with the results of the monitoring and analysis. We propose a scheme for automatically designing the topology and configuration of an entire service with various functional/non-functional requirements. To increase both the flexibility of the requirements and quickness to design, our scheme combines a model-based approach with AI/ML. It generates a huge number of design candidates through the model-based approach, and selects promising candidates efficiently by using AI/ML. We present the architecture of the proposed scheme and experiments we conducted involving a case study. Through the experiment, we confirmed that (1) the design speed was improved by 6 times autonomously, and (2) the increasing speed of design time with respect to the increase of intent size is significantly suppressed.
GraphRR: A multiplex Graph based Reciprocal friend Recommender system with applications on online gaming service

Reciprocal Recommender Systems (RRSs) are recommender systems specifically designed for people-to-people recommendation tasks, e.g., online gaming, dating, and recruitment services. They are fundamentally different from the conventional user–item recommendations. In RRSs, user interactions are usually directional, i.e., they are initiated by one side and not necessarily reciprocated by the other side. In the meanwhile, abundant multiplex user interactions, e.g., Friend Request and Send Message, are collated by the online services and can be represented into a large-scale multiplex user interaction graph. Despite the substantial progress of Graph Neural Networks (GNNs) on capturing users’ multiplex interactions, naive GNNs are insufficient to capture the additional information implied from the directions of interactions, as they are usually not designed to preserve the asymmetric proximities between users. In the paper, we present a novel Graph neural network for Reciprocal Recommendation (GraphRR) to utilize the multiplex user interactions. Specifically, three ego graphs are augmented based on the directions of interactions for each user to capture his preference, attraction and similarity in a finer granularity. Then the multiplexity-aware GNN modules are further applied to measure the contributions of different interaction types. Extensive experiments are conducted in the datasets of the real-world large-scale online games from NetEase Games, a leading game provider for worldwide users. The experimental results demonstrate the superiority of GraphRR over baseline methods and provide empirical evidence for the benefits of the proposed ego graph augmentation. The source code is also available online for reproductivity1.
Implementation of combined steganography and cryptography vigenere cipher, caesar cipher and converting periodic tables for securing secret message

Abstract The development of the communication technology era has made it easier in the process of exchanging information from various parties. However, not all of the information provided can be used by many parties. Sometimes the information is intended for partial purposes only or is confidential. So, it is necessary to process information security. Several ways can be used such as cryptography. This method is used to encrypt messages or information. Methods that can be used in cryptography include vigenere cipher and caesar cipher methods. Both of these methods use the technique of replacing each original character with a character or letter. Substitution is based on keywords or character position shift command. However, this method is quite easy to solve by using the Brute Force method and the frequency of letters that often appear. Based on this problem, it is necessary to modify or combine it so that it is more difficult to crack. This research describes the combination of the vigenere cipher and the caesar cipher methods. The two methods were then converted to the names of the chemical elements in the periodic table. Furthermore, the steganography process is carried out as a place to insert messages or information. From this process, it is expected that the encryption results will be more difficult to crack
Performance Evaluation of the Block Truncation Image Coding with BCH Codes under Noisy Channels

The objective of this paper is to study the performance of block truncation coding and other color quantization algorithms under noisy channels. From the high efficiency variable bit rate BTC coders, some error-correcting bits of shortened BCH codes are added to make a constant bit rate coder. With the modified block, not only some channel distortion can be recovered but error propagating in the data stream is also decreased. The proposed TMM-BTC with shortened BCH codes yield better PSNR than other color quantization coders in a wide range of channel BER.
A new self-supervised task on graphs: Geodesic distance prediction

The heavy dependence on human-curated labels makes supervised algorithms hit a bottleneck in real applications. Fortunately, a more data-efficient learning paradigm—self-supervised learning (SSL) disrupts this dilemma. By creating pretext tasks, self-supervised algorithms learn from the data itself without external supervision. Until recently, SSL has yielded immense success in the image domain, but its research in graph mining has received relatively little scrutiny. In fact, rich topological links and affiliated attributes promise more potential for self-supervised graph learning. Thus, in this paper, we propose a new pretext task called geodesic distance prediction to guide node representation learning. Specifically, this task demands neural nets to learn to infer the geodesic distance of one node relative to the other. Our underlying hypothesis is that doing well on reasoning about the pairwise distance requires the model to extract relation rules between topological distances and attributes. In this way, the complex correlation between nodes can be measured with a simple distance scale. Experiments demonstrate that our S2GRL achieves competitive or better performance than many state-of-the-art self-supervised methods. A case study on distance inference shows that S2GRL can effectively evaluate the risk of business transactions or make recommendations from a topological view.
A Parallel Mapping and Planning Strategy for Micro Aerial Vehicle

Autonomous flight is a fundamental problem for various applications of micro aerial vehicles (MAVs). Thanks to the development of Guidance, Navigation and Control (GNC) technologies, the research on this problem is also becoming mature. However, safe flight in unknown, cluttered environments remains an open question, especially with real-time requirement on onboard computer. This paper proposes a framework including parallelly mapping and planning and implements it on Graphics Processing Unit (GPU). First, a spherical coordinate projection is used in the occupancy grid map to avoid memory conflicts. After that, in the planning phase, a method based on lattice state space sampling is applied to obtain multiple trajectories parallelly. Then, we design a series of soft constraints to ensure that the MAV is in a safe known space with optimal dynamics. By solving the cost for each trajectory and comparison, the optimal trajectory can be generated. The effectiveness of the proposed strategy is demonstrated through simulation tests.
Gravity++: A graph-based framework for constructing interactive visualization narratives

Interactive visualizations play a key part in the exploration and analysis of data, and in the creation of visual data stories. This paper describes a new graph-based framework for developing interactive visualizations for creating coherent visual data stories. We have realized our framework in a prototype tool named Gravity++. Gravity++ uses a novel graph architecture for modeling interaction, data navigation, and changes in visual representation to better communicate findings to an audience. The combination of these graph models provides better support and flexibility for designing interactive visualizations, data navigation, and visual data stories. We demonstrate the applicability of this framework by two example usage scenarios. We also report on an evaluation study conducted with representative participants. All participants successfully created meaningful visual data stories with a high level of complexity. Our results also show that Gravity++ is easy to use and supports the understanding and sense-making of the visual data story creation process.
TADW: Traceable and Anti-detection Dynamic Watermarking of Deep Neural Networks

Deep neural networks (DNN) with incomparably advanced performance have been extensively applied in diverse fields (e.g., image recognition, natural language processing, and speech recognition). Training a high-performance DNN model requires a lot of training data and intellectual and computing resources, which bring a high cost to the model owners. Therefore, illegal model abuse (model theft, derivation, resale or redistribution, etc.) seriously infringes model owners’ legitimate rights and interests. Watermarking is considered the main topic of DNN ownership protection. However, almost all existing watermarking works apply solely to image data. They do not trace the unique infringing model, and the adversary easily detects these ownership verification samples (trigger set) simultaneously. This paper introduces TADW, a dynamic watermarking scheme with tracking and antidetection abilities in the deep learning (DL) textual domain. Specifically, we propose a new approach to construct trigger set samples for antidetection and innovatively design a mapping algorithm that assigns a unique serial number (SN) to every watermarked model. Furthermore, we implement and detailedly evaluate TADW on 2 benchmark datasets and 3 popular DNNs. Experiment results show that TADW can successfully verify the ownership of the target model at a less than 0.5% accuracy cost and identify unique infringing models. In addition, TADW is excellently robust against different model modifications and can serve numerous users.
A New Bilinear Supervised Neighborhood Discrete Discriminant Hashing

Feature extraction is an important part of perceptual hashing. How to compress the robust features of images into hash codes has become a hot research topic. Converting a two-dimensional image into a one-dimensional descriptor requires a higher computational cost and is not optimal. In order to maintain the internal feature structure of the original two-dimensional image, a new Bilinear Supervised Neighborhood Discrete Discriminant Hashing (BNDDH) algorithm is proposed in this paper. Firstly, the algorithm constructs two new neighborhood graphs to maintain the geometric relationship between samples and reduces the quantization loss by directly constraining the hash codes. Secondly, two small rotation matrices are used to realize the bilinear projection of the two-dimensional descriptor. Finally, the experiment verifies the performance of the BNDDH algorithm under different feature types, such as image original pixels and a Convolutional Neural Network (CNN)-based AlexConv5 feature. The experimental results and discussion clearly show that the proposed BNDDH algorithm is better than the existing traditional hashing algorithm and can represent the image more efficiently in this paper.
Multi-objective Learning to Overcome Catastrophic Forgetting in Time-series Applications

One key objective of artificial intelligence involves the continuous adaptation of machine learning models to new tasks. This branch of continual learning is also referred to as lifelong learning (LL), where a major challenge is to minimize catastrophic forgetting, or forgetting previously learned tasks. While previous work on catastrophic forgetting has been focused on vision problems; this work targets time-series data. In addition to choosing an architecture appropriate for time-series sequences, our work addresses limitations in previous work, including the handling of distribution shifts in class labels. We present multi-objective learning with three loss functions to minimize catastrophic forgetting, prediction error, and errors in generalizing across label shifts, simultaneously. We build a multi-task autoencoder network with a hierarchical convolutional recurrent architecture. The proposed method is capable of learning multiple time-series tasks simultaneously. For cases where the model needs to learn multiple new tasks, we propose sequential learning, starting with tasks that have the best individual performances. This solution was evaluated on four benchmark human activity recognition datasets collected from mobile sensing devices. A wide set of baseline comparisons is performed, and an ablation analysis is run to evaluate the impact of the different losses in the proposed multi-objective method. The results demonstrate an up to 4% performance improvement in catastrophic forgetting compared to the use of loss functions in state-of-the-art solutions while demonstrating minimal losses compared to upper bound methods of traditional fine-tuning (FT) and multi-task learning (MTL).
Noise-aware deep learning algorithm for one-stage multispectral pedestrian detection

Multispectral pedestrian detection has gained momentum in the research literature with its wide range of applications in car safety, video surveillance, and robotics. We introduce a noise-aware multispectral detector, called NAMPD, that is based on the one-stage YOLOv4 to produce fast and accurate detection. NAMPD merges the color and thermal streams at the decision level by a weighted sum, where the weight is computed using both the noise level and average luminance of a given image pair. We show that the noise level provides a distinction between nighttime and daytime and can be utilized to improve the detection performance. Experiments are carried out on the sanitized KAIST dataset. They demonstrate that NAMPD is able to achieve a log-average miss rate of 4.25%, which is better than that of the conventional IAF R-CNN, cross-modality interactive attention network, MSDS-RCNN, and MCFF. This mechanism can operate at a speed of 15 fps on the general GPU 1080 Ti, and, thus, is a viable option to be deployed in practical pedestrian detection systems.
Fast Route Planner Considering Terrain Information

Route planning considering terrain information is useful for the navigation of autonomous ground vehicles (AGV) on complicated terrain surfaces, such as mountains with rivers. For instance, an AGV in mountains cannot cross a river or a valley that is too steep. This article addresses a novel route-planning algorithm that is time-efficient in building a sub-optimal route considering terrain information. In order to construct a route from the start to the end point in a time-efficient manner, we simulate two virtual vehicles that deploy virtual nodes iteratively, such that the connected node network can be formed. The generated node network serves as a topological map for a real AGV, and we construct the shortest route from the start to the end point utilizing the network. The route is weighted considering the route length, the steepness of the route, and the traversibility of the route. Through MATLAB simulations, we demonstrate the effectiveness of the proposed route-planning algorithm by comparing it with RRT-star planners.
An Autonomous Control Framework of Unmanned Helicopter Operations for Low-Altitude Flight in Mountainous Terrains

Low-altitude flight in mountainous terrains is a difficult flight task applied in both military and civilian fields. The helicopter has to maintain low altitude to realize search and rescue, reconnaissance, penetration, and strike operations. It contains complex environment perception, multilevel decision making, and multi-objective flight control; thus, flight is currently mainly conducted by human pilots. In this work, a control framework is implemented to realize autonomous flight for unmanned helicopter operations in an unknown mountainous environment. The identification of targets and threats is introduced using a deep neural network. A 3D vector field histogram method is adopted for local terrain avoidance based on airborne Lidar sensors. In particular, we propose an intuitive direct-viewing method to judge and change the visibilities of the helicopter. On this basis, a finite state machine is built for decision making of the autonomous flight. A highly realistic simulation environment is established to verify the proposed control framework. The simulation results demonstrate that the helicopter can autonomously complete flight missions including a fast approach, threat avoidance, cover concealment, and circuitous flight operations similar to human pilots. The proposed control framework provides an effective solution for complex flight tasks and expands the flight control technologies for high-level unmanned helicopter operations.
FabToys

Recent advances in fabric-based sensors have made it possible to densely instrument textile surfaces on smart toys without changing their look and feel. While such surfaces can be instrumented with traditional sensors, rigid elements change the nature of interaction and diminish the appeal of plush toys.
Approach for Preprocessing in Offline Optical Character Recognition (OCR)

Offline optical character recognition (Offline OCR) is one of the important applications of pattern recognition. To achieve a better recognition result, the input character images must have good quality. That is why the preprocessing step be-comes essential for any image identification task. Lots of research has been performed in numerous jobs towards this preprocessing in the literature. Here, an attempt has been made to summarize different procedures and aspects of preprocessing adopted in implementing these preprocessing techniques. This is done in the hope that this may help the research community towards the gaining of knowledge of different preprocessing techniques used in offline OCR. Offline OCR has several applications, such as old manuscript digitization, signature authentication, bank cheque automatic clearance and postal letter sorting, etc. Finally, an overall summary in a concise way has been presented based on different preprocessing techniques used in offline OCR.
Salami Slicing: clarifying common misconceptions for social science early-career researchers

Abstract The term Salami Slicing is used often within academia to refer to the needless separation of a single research study, attached dataset, and argument that should form a single publication, into two or several separate publications for submission in different journals. While the term is used with frequency, there exist few explicit guidelines that concretely explain firstly: what tangibly constitutes the unethical and damaging practice of Salami Slicing, and secondly: instances relating to social science research where multiple publications from a single body of data are not only acceptable, but necessary for furthering scholarly thought and developing important cross-disciplinary perspectives. These cases actively exist outside of and resist the Salami Slicing label. This paper represents a discussion of the above points from my perspective as a social science early career researcher (ECR) in the hope of clarifying the key misconceptions and ambiguities surrounding this common yet often elusive term. By doing this, I hope to minimise anxiety and panic for fellow ECRs, and help them avoid needlessly withdrawing carefully developed and time-intensive publications that could actively benefit and grow different disciplinary social science perspectives.
Financial Fraud Detection Approach Based on Firefly Optimization Algorithm and Support Vector Machine

The usage of credit cards is increasing daily for online transactions to buy and sell goods, and this has also increased the frequency of online credit card fraud. Credit card fraud has become a serious issue for financial institutions over the last decades. Recent research has developed a machine learning (ML)-based credit card fraud transaction system, but due to the high dimensionality of the feature vector and the issue of class imbalance in any credit card dataset, there is a need to adopt optimization techniques. In this paper, a new methodology has been proposed for detecting credit card fraud (financial fraud) that is a hybridization of the firefly bio-inspired optimization algorithm and a support vector machine (called FFSVM), which comprises two sequential levels. In the first level, the firefly algorithm (FFA) and the CfsSubsetEval feature section method have been applied to optimize the subset of features, while in the second level, the support vector machine classifier has been used to build the training model for the detection of credit card fraud cases. Furthermore, a comparative study has been performed between the proposed approach and the existing techniques. The proposed approach has achieved an accuracy of 85.65% and successfully classified 591 transactions, which is far better than the existing techniques. The proposed approach has enhanced classification accuracy, reduced incorrect classification of credit card transactions, and reduced misclassification costs. The evaluation results show that the proposed FFSVM method outperforms other nonoptimization machine learning techniques.