Reviewerly/exHarmony huggingface dataset:
Offline Real-World Wireless Interference Signal Classification Algorithm Utilizing Denoising Diffusion Probability Model

The dependable transmission of data and the efficacy of wireless communication depend critically on the detection and classification of interference. Traditional classification algorithms may not yield the requisite precision in identifying and categorizing diverse types of interference, whereas deep learning (DL) algorithms necessitate high-quality data and training samples, which prove unfeasible in real-time communication scenarios. In addressing these challenges, we present a novel approach that utilizes the denoising diffusion probabilistic model (DDPM) for offline processing of collected signals before feature extraction and subsequently sending the signals into a predefined classifier. Our experimental analyses show that our approach delivers up to 91% accuracy without any prior information, outperforming both generative adversarial network (GAN)-based and other traditional DL algorithms, even with limited signal samples of only 5. More significantly, our approach underscores the feasibility of employing generative models in signal processing and achieves state-of-the-art performance on high-precision recognition in real-world communication scenarios.
Phase-shifting projected fringe profilometry using the pulsed phase-encoding algorithm

A phase unwrapping method using the phase-encoded algorithm for phase-shifting projected fringe profilometry is presented. There is no need to take additional projections for phase unwrapping. The patterns used to perform the phase-extraction can be employed for unwrapping directly.
Steganalysis of adaptive multi-rate speech with unknown embedding rates using clustering and ensemble learning

With the widespread use of adaptive multi-rate (AMR) speech-based applications, AMR speech-based steganography has witnessed significant growth. Consequently, steganalysis approaches have garnered attention to mitigate network security risks associated with AMR speech-based steganography. However, existing studies often assume known embedding rates of test samples, leaving steganography detection under unknown embedding rates—an encountered practical scenario—unresolved. To tackle this challenge, this paper presents a novel detection scheme for AMR speech-based steganography, skillfully combining clustering and ensemble learning. The training phase utilizes K-means clustering to pre-classify speech samples, grouping them into distinct clusters based on their feature distribution and embedding rates. Subsequently, a classifier based on extreme gradient boosting (XGBoost) is trained for each cluster. The experimental results demonstrate that the proposed scheme exhibits significant improvements in terms of recall rate when compared to existing steganalysis techniques.
Cataractous eye fundus image enhancement by using multi-scale tone decomposition

Through optical equipment such as the ophthalmoscope, it is possible to visualize and image the inner surface of the eye, where the main structures of the retina can be observed. The visual analysis of the retinal vasculature is widely used by ophthalmologists for prevention, diagnosis, and monitoring of retinal diseases. Nevertheless, derived from pathologies that generate an opacity in the crystalline lens (such as cataracts), the task of visualize blood vessels becomes difficult, since there is a lack of contrast in the fundus image. In this work, a multiscale decomposition method based on the Weighted Least Squares (WLS) optimization is applied to cataractous eye fundus images, with the aim of obtaining a better blood-vessel to background contrast. The proposed scheme is implemented over a publicly-available cataract eye fundus dataset. The experimental results provide a notorious visual improvement in contrast and restoration of blood vessels pixels and, in addition, maintains adequate saturation and lighting for visual analysis. The visual improvement of the vasculature represents a potential benefit in the ophthalmic analysis of patients with cataracts, since it is possible to observe the vascular morphology in greater detail while keeping relevant image features.
3D profile measurements using 2D fringe projections in the tele-centric system

A 2D fringe pattern is presented for projected fringe profilometry in the tele-centric system. Compared with the methods which embeds multiple frequencies in one pattern, the proposed one-shot method is more tolerant to low signal-to-noise ratios and more reliable for surface with large color or reflectance variation.
Hopping discrete cosine transform

The paper deals with the design of a fast algorithm for computing the hopping discrete cosine transform in equidistant signal windows using a recursive relationship between transform spectra. Discrete cosine transform is widely used in digital signal processing such as image coding, spectral analysis, feature extraction, and filtering. Short-time transform is suitable for adaptive processing and time-frequency analysis of quasi-stationary data. Hopping transform refers to a transform computed on the signal of a fixed-size window that slides over the signal with an integer hop step. Hopping discrete transform can be employed for time-frequency analysis and adaptive processing quasi-stationary data such as speech, biomedical, radar and communication signals. The performance of the algorithm with respect to computational costs and execution time is compared with that of conventional sliding and fast algorithms.
Deep neural network for incongruent point clouds registration

Recently, there has been essential progress in the field of deep learning, which has led to compelling advances in most of the semantic tasks of computer vision, such as classification, detection, and segmentation. Point cloud registration is a task that aligns two or more different point clouds by evaluating the relative transformation between them. The Iterative Closest Points (ICP) algorithm and its variants have relatively good computational efficiency but are known to be subject to local minima, so rely on the quality of the initialization. In this paper, we propose a neural network based on the Deep Closest Points (DCP) neural network to solve the point cloud registration problem for incongruent point clouds. Computer simulation results are provided to illustrate the performance of the proposed method.
Shearing interferometry and its implications for freeform characterization

This work explores combining the reference sphere and nulling hologram for freeform measurements by using a nulling reflection hologram in a Fizeau geometry. To do this, we combined standard phase shifting with an iterative algorithm to solve for errors in the phase steps, obtained a phase surface, and ultimately fit it to a set of Zernike polynomials. Preliminary results show that we can characterize a surface via Zernike coefficients using in situ holographic exposures followed by a sequence of interferograms using small (&lt;100&mu;m) values of lateral shear.
Super-Resolution of Wireless Channel Characteristics: A Multitask Learning Model

Channel modeling has always been the core part of the design and development of communication system, especially in the sixth-generation (6G) era. Traditional approaches like stochastic channel modeling and ray-tracing (RT)-based channel modeling depend heavily on measurement data or simulation, which are usually expensive and time-consuming. In this article, we propose a super-resolution (SR) model for recovering high-resolution (HR) channel characteristics from sparse sampling data. The model is based on multitask learning (MTL) convolutional neural networks (CNNs) with attention mechanism and residual connection. Experiments demonstrate that the proposed MTL SR model could achieve fairly good performances in terms of mean absolute error (MAE) and standard deviation of error (STDE). The advantages of the proposed model are demonstrated in comparison with other state-of-the-art deep learning (DL) models and visualization of SR performances. The ablation study also proved the necessity of data augmentation and the techniques in the model design. The good generalizability of the proposed MTL SR model leads to discussions of potential applications. The contribution of this article could be helpful in channel modeling, network optimization, positioning, and other wireless channel characteristics-related work by largely reducing the workload of simulation or measurement.
Key Spatio-Temporal Energy Information Mapping for Action Recognition

Nowadays, human action recognition (HAR) based on depth video sequences has become a popular research topic. However, some existing feature map representations suffer from the issue of missing temporal information and cannot completely represent the spatio-temporal information. Moreover, the redundant information is not effectively removed, and the key information cannot be highlighted. To address these issues, we propose an effective method called key spatio-temporal energy information mapping (KSTEIM). First, we construct a human motion energy field to strengthen the distribution of motion energy. Next, we present a new key frame algorithm that removes the redundant frames based on redundancy values calculated from image differences to obtain keyframe sequences. Then, each key frame is projected onto three orthogonal axes to obtain three 1-D motion energy lists. The energy lists are concatenated in temporal sequence under each projection axis to form KSTEIM. Then, we extract histogram of oriented gradients (HOG) from KSTEIM and make KSTEIM-HOG sparser through multi-kernel subspace representation enhancement (MKSRE). With only positive sequence actions, our method achieves a recognition rate of 93.41% on Microsoft research Action3D (MSR-Action3D), and 90.93% on the University of Texas at Dallas Multimodal Human Action Dataset (UTD-MHAD). It indicates that the performance of our method is no weaker than most existing methods.
Knowledge-Driven Semantic Segmentation for Waterway Scene Perception

Semantic segmentation as one of the most popular scene perception techniques has been studied for autonomous vehicles. However, deep learning-based solutions rely on the volume and quality of data, and knowledge from specific scene might not be incorporated. A novel knowledge-driven semantic segmentation method is proposed for waterway scene perception. Based on the knowledge that water is irregular and dynamically changing, a life time-of feature (LToF) detector is designed to distinguish water region from surrounding scene. Using a Bayesian framework, the detector as the likelihood function is combined with U-Net-based semantic segmentation to achieve an optimized solution. Finally, two public datasets and typical semantic segmentation networks, FlowNet, DeepLab, and DVSNet are selected to evaluate the proposed method. Also, the sensitivity of these methods and ours to the dataset is discussed.
BAN: Predicting APT Attack Based on Bayesian Network With MITRE ATT&amp;CK Framework

Since cyberattacks have become sophisticated in the form of advanced persistent threats (APTs), predicting and defending the APT attacks have drawn lots of attention. Although there have been related studies such as attack graphs, Hidden Markov Models, and Bayesian networks, they have four representative limitations; (i) non-standard attack modeling, (ii) lack of data-driven approaches, (iii) absence of real-world APT dataset, and (iv) high system dependability. In this paper, we propose Bayesian ATT&CK Network (BAN) which is based on system-independent data-driven approach. Specifically, BAN is based on Bayesian network, which adopts structure learning and parameter learning to model APT attackers with the MITRE ATT&CK® framework. The trained BAN aims to predict upcoming attack techniques and derives corresponding countermeasures. In addition, we prepare datasets via both automatic and manual labeling to overcome the data insufficiency issues of APT prediction. Experimental results show that BAN successfully contributes to handling APT attacks, given the best parameters extracted from extensive evaluations.
LC-VTON: Length Controllable Virtual Try-on Network

Image-based virtual try-on provides customers with convenient online clothes selections by transferring garments onto a reference person. Despite the emergence of several solutions to generate photo-realistic images and adapt to complex poses, controlling clothing length remains a challenge. We argue that the clothing reconstruction did not consider clothing length information, which results in clothing length being uncontrollable in most virtual try-on methods. To overcome this limitation, a novel clothing-agnostic person representation is proposed, which eliminates clothing information and quantifies clothing length as a numerical value. A new segmentation generator is designed to predict try-on segmentation maps of any length conditioned on this representation. Moreover, we correct two inaccurate labels, which enables our model to generate a wider range of garment interactions in images, such as the top tucked into or worn over the bottom, as well as the top and bottom worn separately without intersecting. Extensive experiments demonstrate that our method achieves the goal of continuous clothing length control and generates photo-realistic images with fine details that outperform most baseline methods in terms of quantitative and qualitative metrics.
A Hardware Architecture of NIST Lightweight Cryptography Applied in IPSec to Secure High-Throughput Low-Latency IoT Networks

The Internet-of-things (IoT) has rapidly grown in recent years, making it an integral part of many areas of our lives. Many IoT networks require high data throughput and low latency, allowing for real-time communication and data transmission, enabling improved efficiency, cost savings, and enhanced decision-making capabilities in various industries such as manufacturing, healthcare, transportation, and smart cities. However, with the increasing amount of data being transmitted, the security of high-speed IoT networks becomes a critical concern. In this paper, we proposed a hardware architecture for Ascon, a NIST Lightweight cryptography standard to enable high-throughput, low-latency security services in IPSec protocols. Results show that the ESP protocol can achieve a maximum throughput of 8.806 Gbps and a minimum latency of 427ns for only 2812 Slice. This ESP core together with the proposed Ascon implementation can be used in IoT gateways to provide security services for high-speed, low-latency IoT networks.
Convolutional auto-encoder to extract local features of 2D images

The important task of 2D image classification and segmentation is the extraction of the local geometrical features. The convolution neural network is the common approach last years in this field. Usually, the neighborhood of each pixel of the image is implemented to collect local geometrical information. The information for each pixel is stored in a matrix. Then, Convolutional Auto-Encoder (CAE) is utilized to extract the main geometrical features. In this paper, we propose a neural network based on CAE to solve the extraction of local geometrical features problem for noisy images. Computer simulation results are provided to illustrate the performance of the proposed method.
Any-to-Any Voice Conversion With Multi-Layer Speaker Adaptation and Content Supervision

Any-to-any voice conversion can be performed among arbitrary speakers, even with a single reference utterance. Many related studies have demonstrated that it can be effectively implemented by speech representation disentanglement. However, most existing solutions fuse the speaker representations into the content features globally without considering their distribution difference. Additionally, in the any-to-any scenario, there is no effective method ensuring the consistency of linguistic content without text transcription or additional information extracted from additional modules (e.g., automatic speech recognition). Hence, to alleviate the above problems, this paper proposes SACS-VC, a novel any-to-any voice conversion method that combines two principal modules: Speaker Adaptation and Content Supervision. Specifically, we rearrange the timbre representations according to the content distribution using a temporal attention mechanism to obtain finer-grained speaker timbre information for each content feature. Meanwhile, we associate the converted outputs and source utterances directly to supervise the consistency of the semantic content in an unsupervised manner. This is achieved using contrastive learning based on the corresponding and non-corresponding locations of content features. It should be noted that SACS-VC can be implemented using a non-parallel speech corpus without any pertaining. The experimental results demonstrate that the proposed method outperforms current state-of-the-art any-to-any voice conversion systems in objective and subjective evaluation settings.
Enriching Transformer-Based Embeddings for Emotion Identification in an Agglutinative Language: Turkish

Text-based emotion detection is an important and expanding research area due to the increasing accessibility of written data via the Internet and social media. Vector space models, such as semantic and contextual methods, are frequently used in many domains in natural language processing. Currently, to improve performance in emotion/sentiment detection studies, a new research area has emerged, which involves adding extra emotion information (emotion enrichment) to these models. Furthermore, as emotion depends on multiple parameters, the success of enrichment may vary based on different languages. In this study, we applied two emotion-enrichment methods on emerging transformer-based models [bidirectional encoder representations from transformers (BERT), a robustly optimized BERT pretraining approach, a distilled version of BERT, and efficiently learning an encoder that classifies token replacements accurately] and a traditional semantic model (Word2Vec) (as a baseline) on the Turkish (a highly agglutinative language) dataset. The performance was analyzed with classification models and cosine-similarity metrics.
The Impact of Silence on Speech Anti-Spoofing

The current speech anti-spoofing countermeasures (CMs) show excellent performance on specific datasets. However, removing the silence of test speech through Voice Activity Detection (VAD) can severely degrade performance. In this paper, the impact of silence on speech anti-spoofing is analyzed. First, the reasons for the impact are explored, including the proportion of silence duration and the content of silence. The proportion of silence duration in spoof speech generated by text-to-speech (TTS) algorithms is lower than that in bonafide speech. And the content of silence generated by different waveform generators varies compared to bonafide speech. Then the impact of silence on model prediction is explored. Even after retraining, the spoof speech generated by neural network based end-to-end TTS algorithms suffers a significant rise in error rates when the silence is removed. To demonstrate the reasons for the impact of silence on CMs, the attention distribution of a CM is visualized through class activation mapping (CAM). Furthermore, the implementation and analysis of the experiments masking silence or non-silence demonstrates the significance of the proportion of silence duration for detecting TTS and the importance of silence content for detecting voice conversion (VC). Based on the experimental results, improving the robustness of CMs against unknown spoofing attacks by masking silence is also proposed. Finally, the attacks on anti-spoofing CMs through concatenating silence, and the mitigation of VAD and silence attack through low-pass filtering are introduced.
Mechanisms for Improving Investment Efficiency through Continuous Delivery in Internet Services

Many Internet service companies are working to speed up the delivery of new functions in order to gain a competitive advantage. One method that is gaining attention is Continuous Delivery (CD). Shorter delivery cycles are considered better. Inspired by the case of software development at Recruit Co., Ltd. this research proposes a new computational model that expresses delivery cycles in terms of time and profitability. We then show that profitability can be improved by shortening the CD cycle term and dividing development items. We show that this improvement occurs by minimizing opportunity losses. These results provide important implications for the implementation of CD and suggest directions for future research.
Have the Factors Affecting Software New Product Development (S-NPD) Changed in the Age of Mobile Apps and Agile Methods?

It has been long recognized that Time-to-Market, Cost-of-Delay, and Uncertainty are the three major factors that can impact New Product Development (NPD) process. Software New Product Development (S-NPD) process is even more notorious with delay and cost overrun. The goal of this position paper is to examine the three factors: Time-to-Market, Cost-of-Delay and Uncertainty through “observation” vis-à-vis software Apps development process and the use of Agile methods in order to see if something has changed over the years. In addition, a controlled group interview is conducted to support the observation method. The questions this paper seeks to answer are: Are we getting to market on time? Are we delivering software product on budget? Do we have reduced uncertainty when it comes to Apps development and the use of Agile method? The results obtained show that the use of Agile approach in developing Apps can take the product to the market quicker compared to using the rigid linear process, in our case the waterfall V-model. Also, the “product introduction delay” as part of product performance was measured on the assumption that there is no budget or cost overrun. In this case, the Agile method performs better than the linear approach. Lastly, in terms of technological uncertainty, the Agile development approach does not necessarily bring the expected commercial success, and in fact, for the situation where the development of Apps is involved, rapid development could result in failure if proper plan is not in place for the evolution of the product.
Artificial Intelligence Innovation Assistant

Innovation management (IM) practices are a key element to engage the fast pace of technological momentum. Firms look to keep competitive advantages ultimately to achieve their strategic objectives. Consequently, enhancing and reinventing such practices is fundamental in order to exploit the disruptive capabilities of the emerging artificial intelligence (AI) era.We introduce AIIA (artificial intelligence innovation assistant), as a tool capable to implement artificial intelligence insights into some of the most widely adopted organizational IM processes. As a system of systems model, AIIA depicts a simple yet powerful proposition to integrate state of the art data processing algorithms and management practices. Unlike other current IM models AIIA embraces innovation from a holistic and cohesive perspective for enterprises and entrepreneurs who seek to incorporate AI into their regular IM activities.Despite AIIA’s novelty, future research is required to tackle numerous limitations of this model. Nevertheless, evidence shows AIIA serves as a platform to allow the automated manipulation of data during innovation management processes in a structured and flexible way. Most importantly, this model supports firms and companies as a facilitator to introduce and launch a new stage of technological innovation management capabilities.