Reviewerly/exHarmony huggingface dataset:
Validation of a Machine Learning Expert Supporting System, ImmunoGenius, Using Immunohistochemistry Results of 3000 Patients with Lymphoid Neoplasms

(1) Background: Differential diagnosis using immunohistochemistry (IHC) panels is a crucial step in the pathological diagnosis of hematolymphoid neoplasms. In this study, we evaluated the prediction accuracy of the ImmunoGenius software using nationwide data to validate its clinical utility. (2) Methods: We collected pathologically confirmed lymphoid neoplasms and their corresponding IHC results from 25 major university hospitals in Korea between 2015 and 2016. We tested ImmunoGenius using these real IHC panel data and compared the precision hit rate with previously reported diagnoses. (3) Results: We enrolled 3052 cases of lymphoid neoplasms with an average of 8.3 IHC results. The precision hit rate was 84.5% for these cases, whereas it was 95.0% for 984 in-house cases. (4) Discussion: ImmunoGenius showed excellent results in most B-cell lymphomas and generally showed equivalent performance in T-cell lymphomas. The primary reasons for inaccurate precision were atypical IHC profiles of certain cases, lack of disease-specific markers, and overlapping IHC profiles of similar diseases. We verified that the machine-learning algorithm could be applied for diagnosis precision with a generally acceptable hit rate in a nationwide dataset. Clinical and histological features should also be taken into account for the proper use of this system in the decision-making process.
Penguins Go Parallel: A Grammar of Graphics Framework for Generalized Parallel Coordinate Plots

Parallel Coordinate Plots (PCP) are a valuable tool for exploratory data analysis of high-dimensional numerical data. The use of PCPs is limited when working with categorical variables or a mix of categorical and continuous variables. In this article, we propose Generalized Parallel Coordinate Plots (GPCP) to extend the ability of PCPs from just numeric variables to dealing seamlessly with a mix of categorical and numeric variables in a single plot. In this process we find that existing solutions for categorical values only, such as hammock plots or parsets become edge cases in the new framework. By focusing on individual observations rather than a marginal frequency we gain additional flexibility. The resulting approach is implemented in the R package ggpcp. Supplementary materials for this article are available online.
Privacy-preserving federated learning on lattice quantization

Federated learning (FL) is an important approach to cooperate with multiple devices for learning without exchanging data between devices and central server. However, due to bandwidth and other reasons, the communication efficiency should be considered when the volume of information transmitted is limited. In this paper, we utilize the tool of lattice quantization form quantization theory and the variable intercommunication interval to improve communication efficiency. Meanwhile, to make strong privacy guarantee, we incorporate the notion of differential privacy (DP) to the FL framework with local SGD algorithm. By adding calibrated noises, we propose a universal lattice quantization for differentially private federated averaging algorithm (ULQ-DP-FedAvg). We provide tight privacy bound by using some privacy techniques. We also analyze the convergence bound of ULQ-DP-FedAvg based on bits rate constraints and the growing inter-communication interval as well as the data are non-independent identically distribution (Non-IID). It turns out that the algorithm converges and preserves that the privacy has scarcely influenced on the convergence rate. The effectiveness of our algorithm is demonstrated by synthetic and real datasets.
The Role of Transliterated Words in Linking Bilingual News Articles in an Archive

Retrieving a specific digital information object from a multi-lingual huge and evolving news archives is challenging and complicated against a user query. The processing becomes more difficult to understand and analyze when low-resourced and morphologically complex languages like Urdu and Arabic scripts are included in the archive. Computing similarity against a query and among news articles in huge and evolving collections may be inaccurate and time-consuming at run time. This paper introduces a Similarity Measure based on Transliteration Words (SMTW) from the English language in the Urdu scripts for linking news articles extracted from multiple online sources during the preservation process. The SMTW link Urdu-to-English news articles using an upgraded Urdu-to-English lexicon, including transliteration words. The SMTW was exhaustively evaluated to assess the effectiveness using different size datasets and the results were compared with the Common Ratio Measure for Dual Language (CRMDL). The experimental results show that the SMTW was more effective than the CRMDL for linking Urdu-to-English news articles. The precision improved from 50% to 60%, recall improved from 67% to 82%, and the impact of common terms also improved.
LabanFormer: Multi-scale graph attention network and transformer with gated recurrent positional encoding for labanotation generation

Labanotation is a widely-used notation system for recording human dance movements. Automatically generating Labanotation scores from motion capture data can save significant manual effort and help the preservation of old folk dances in protecting intangible cultural heritages. Existing Labanotation generation methods have limited ability to capture the flexible limb movements as well as the rich periodic, symmetric, or repeated dance steps. In this paper, we present a novel LabanFormer model including a Multi-Scale Graph Attention network (MS-GAT) and a transformer model with Gated Recurrent Positional Encoding (GRPE) to achieve more effective Labanotation generation. First, the proposed MS-GAT can capture flexible limb movements by learning feature correlations between every two joints and aggregating features of neighboring joints over multiple scales. Second, we propose a new GRPE-based transformer to learn global temporal dependencies in the output feature sequences of MS-GAT. The novel GRPE module can encode position information with learnable parameters while handling various sequence lengths. As such, the periodic, symmetric, or repeated steps in dances can be accurately captured. Finally, the corresponding Laban symbols are generated by the decoder of the GRPE-based transformer. Extensive experiments on two real-world datasets show that the proposed LabanFormer model obtains remarkable performance compared with state-of-the-art approaches on the automatic Labanotation generation task.
Cryptographic Encryption and Optimization for Internet of Things Based Medical Image Security

The expansion of the Internet of Things is expected to lead to the emergence of the Internet of Medical Things (IoMT), which will revolutionize the health-care industry (IoT). The Internet of Things (IoT) revolution is outpacing current human services thanks to its bright mechanical, economical, and social future. Security is essential because most patient information is housed on a cloud platform in the hospital. The security of medical images in the Internet of Things was investigated in this research using a new cryptographic model and optimization approaches. For the effective storage and safe transfer of patient data along with medical images, a separate framework is required. The key management and optimization will be chosen utilizing the Rivest–Shamir–Adleman-based Arnold map (RSA-AM), hostile orchestration (HO), and obstruction bloom breeding optimization (OBBO) to increase the encryption and decryption processes’ level of security. The effectiveness of the suggested strategy is measured using peak signal-to-noise ratio (PSNR), entropy, mean square error (MSE), bit error rate (BER), structural similarity index (SSI), and correlation coefficient (CC). The investigation shows that the recommended approach provides greater security than other current systems.
Malicious Software Detection based on URL-API Intensity Feature Selection Using Deep Spectral Neural Classification for Improving Host Security

In recent years, the internet of services has been more responsive to access through the development of various application program interfaces (API). Accessing an HTTP uniform resource locator (URL) contains malicious software intended by the attacker to create security breaches through the use of APIs from various services on the internet. By default, the non-attentive URL downloads and installs malware in the background without the user’s knowledge. The host does not analyze the API-URL security certificate contract due to the feature access by the user. Therefore, the current Machine Learning (ML) techniques only check malware signatures and certificates rather than analyzing URL behaviour based on the impact of a URL accessed from the internet. To address this problem, we propose a novel intelligent malicious software based on URL-API intensity feature selection (IFS) and deep spectral neural classification (DSNC) for improving Host Security. Initially, the URL — successive certificate signing (SCS) of the user link accessibility is verified based on API download rate logs. This system identifies the best malware software. The Link Redirection Stability Rate (LRSR) is estimated based on the Redirection URL by accessing the direct link and redirect link. The domain transformation matrix (DTM) was created to create a pattern to access successive features. URL-API Intensity Feature Selection selects each estimated feature, and the selected features are based on soft-max logical activation with a recurrent neural network (RNN) optimized for deep learning. RNN is trained in the spectral domain for improving computation and efficiency. It predicts the class based on the risk of malicious weight to categorize class by reference. The proposed IFS-DSNC achieves accuracy of 95.6% than the other algorithms such as KNN, NB, CNN, LCS, GCRNC AGSCR. The experimental result shows that the proposed method provides better performance in finding malware software than the existing approaches, thereby improving the security against host breaching.
High-Embedded Low-Distortion Multihistogram Shift Video Reversible Data Hiding Based on DCT Coefficient

Video reversible data hiding technology can be applied to copyright protection, medical images, the military, and other fields, but it cannot guarantee high visual quality with an effective embedded capacity. In this paper, a high-embedding and low-distortion reversible data hiding scheme based on a discrete cosine transform (DCT) coefficients method is proposed. The scheme first decodes the original video stream with entropy, obtains all the DCT blocks, and selects the embeddable DCT blocks according to the capacity of the zero factor. Then, it divides the coefficients in the DCT blocks into the shift and embedding coefficients. The shift coefficients directly generate a one-dimensional histogram; the embedding coefficients generate a two-dimensional histogram according to paired strategies. Finally, the secret data can be successfully embedded according to the proposed two-dimensional histogram shift reversible data hiding scheme. This scheme performed more effectively than existing schemes in terms of the embedded capacity, peak signal-to-noise ratio (PSNR), and structural similarity index measure (SSIM).
Communicative Learning: A Unified Learning Formalism

In this article, we propose a communicative learning (CL) formalism that unifies existing machine learning paradigms, such as passive learning, active learning, algorithmic teaching, and so forth, and facilitates the development of new learning methods. Arising from human cooperative communication, this formalism poses learning as a communicative process and combines pedagogy with the burgeoning field of machine learning. The pedagogical insight facilitates the adoption of alternative information sources in machine learning besides randomly sampled data, such as intentional messages given by a helpful teacher. More specifically, in CL, a teacher and a student exchange information with each other collaboratively to transmit and acquire certain knowledge. Each agent has a mind, which includes the agent’s knowledge, utility, and mental dynamics. To establish effective communication, each agent also needs an estimation of its partner’s mind. We define expressive mental representations and learning formulation sufficient for such recursive modeling, which endows CL with human-comparable learning efficiency. We demonstrate the application of CL to several prototypical collaboration tasks and illustrate that this formalism allows learning protocols to go beyond Shannon’s communication limit. Finally, we present our contribution to the foundations of learning by putting forth hierarchies in learning and defining the halting problem of learning.
Signal Game Analysis between Software Vendors and Third-Party Platforms in Collaborative Disclosure of Network Security Vulnerabilities

The global network threat is becoming more and more serious, and network security vulnerability management has become one of the critical areas in the national information security emergency system construction. To guide the third-party sharing platforms regarding network security vulnerability management, this work constructs a signal game model comprising third-party vulnerability sharing platforms and software vendors for vulnerability collaborative disclosures. In addition, we analyze the game strategy selection and its influencing factors. The results show that there are two perfect Bayesian equilibria, including separation equilibrium and mixed equilibrium, due to the incomplete lines of information disclosure. The equilibrium state is mainly based on the compression time of the protection period and the existence ratio of the software vendors who develop the patches in the market. This work puts forward some suggestions in terms of the protection period, reputation loss, and relevant laws and regulations.
Identifying effective trajectory predictions under the guidance of trajectory anomaly detection model

Trajectory Prediction (TP) is an important research topic in computer vision and robotics fields. Recently, many stochastic TP models have been proposed to deal with this problem and have achieved better performance than the traditional models with deterministic trajectory outputs. However, these stochastic models can generate a number of future trajectories with different qualities. They are lack of self-evaluation ability, that is, to examine the rationality of their prediction results, thus failing to guide users to identify high-quality ones from their candidate results. This hinders them from playing their best in real applications. In this paper, we make up for this defect and propose TPAD, a novel TP evaluation method based on the trajectory Anomaly Detection (AD) technique. In TPAD, we firstly combine the Automated Machine Learning (AutoML) technique and the experience in the AD and TP field to automatically design an effective trajectory AD model. Then, we utilize the learned trajectory AD model to examine the rationality of the predicted trajectories, and screen out good TP results for users. Extensive experimental results demonstrate that TPAD can effectively identify near-optimal prediction results, improving stochastic TP models' practical application effect.
Action decoupled SAC reinforcement learning with discrete-continuous hybrid action spaces

Most existing Deep Reinforcement Learning (DRL) algorithms solely apply to discrete action or continuous action spaces. However, the agent often has both continuous and discrete action space, named hybrid action space. This paper proposes an action-decoupled algorithm for hybrid action space. Specifically, the hybrid action is decoupled, and then the original agent in the hybrid action space is abstracted into two agents. Each agent contains only discrete or continuous action space. The discrete and continuous actions are independent of each other to be executed simultaneously. We use the Soft Actor-Critic (SAC) algorithm as the optimization method and name our proposed algorithm Action Decoupled SAC (AD-SAC). We handle multi-agent problems using a framework of Centralized Training Decentralized Execution (CTDE) and then reduce the concatenation of partial agent observations to avoid the interference of redundant observations. We design a hybrid action space environment for Unmanned Aerial Vehicles (UAVs) path planning and gimbal scanning using AirSim. The results show that our algorithm has better convergence and robustness than the discretization, relaxation, and the Parametrized Deep Q-Networks Learning (P-DQN) algorithms. Finally, we carried out a Hardware in the Loop (HITL) simulation experiment based on Pixhawk to verify the feasibility of our algorithm.
Know your enemy: Conversational agents for security, education, training, and awareness at scale

Social engineering attacks are widespread, costly, and challenging to mitigate through technical means. Companies allocate considerable resources toward security education, training, and awareness. However, the cost of these efforts is high, and the efficacy is questionable, particularly for defending against social engineering attacks. To improve security training for employees against social engineering at scale, we have designed a conversational agent to simulate a person becoming the victim of a social engineering attack. We build on Utility theory and previous social engineering, adversarial thinking, and conversational agent research. We designed a conversational agent to allow humans to learn and practice social engineering techniques in a fun and engaging setting. Users learn how an attacker might use these techniques by performing them. We present the design of a conversational agent for social engineering training and demonstrate a process to measure adversarial thinking in a computer-mediated conversational context. We showed that the conversational agent performs well and accurately measures adversarial thinking through two studies.
An improvised nature-inspired algorithm enfolded broad learning system for disease classification

Deep analysis of genomic data reveals that many deadly diseases are generated due to genetic mutation. To make the health care system more robust, a machine learning researcher’s prime intention is to classify the genomic data more efficiently within less time. As the genomic data suffers from the malediction of excessive dimensionality, the selection of the notable genes is always a big challenge for the researcher. The selection of prominent genomic key attributes by any nature-inspired learning algorithm always remains a non-deterministic polynomial-time (NP-Hard) problem. Therefore, there is always a scope to apply new algorithms. In this projected work, an improvised sine–cosine hybridized Monarch Butterfly Optimization (SC-MBO) algorithm, is embedded with the Broad Learning System (BLS), which is defined as SC-MBO-BLS, for choosing the most significant genes and classifying the genomic data simultaneously. Initially, Kernel-based Fisher Score (K-FS) is applied to select notable genes. Then, the selected genes further undergoes for execution using the SC-MBO-BLS model. To prove the effectiveness of the suggested model, ten cancerous genomic data are considered. Here, several performance evaluators (i.e., precision, MCC, sensitivity, Kappa, F-score, and specificity) are applied for unbiased comparison. This presented model is compared with SC-MBO wrapped Multilayer Perceptron (SC-MBO-MLP), SC-MBO wrapped Extreme Learning Machine (SC-MBO-ELM), and SC-MBO wrapped Kernel Extreme Learning Machine (SC-MBO- KELM) and yields the highest accuracy in ten datasets such as 100%, 98.4%, 99%, 99.6%, 100, 97.2%, 100%, 100%, 98.6%, 99.5% in Leukemia, Colon tumor, Breast cancer, Ovarian cancer, Lymphoma-3, MLL, ALL-AML-3, SRBCT, ALL-AML-4 and Lung cancer respectively. Further, the existing twenty standard models are taken for comparison with the suggested model. Additionally, to assess the presented model, a statistical method i.e., Analysis of variance (ANOVA) is considered. As per the above quantitative and qualitative estimation, it is deduced that the suggested SC-MBO-BLS approach outclasses other considering models.
Neural Network-Based Identification of Cloud Types from Ground-Based Images of Cloud Layers

Clouds are a significant factor in regional climates and play a crucial role in regulating the Earth’s water cycle through the interaction of sunlight and wind. Meteorological agencies around the world must regularly observe and record cloud data. Unfortunately, the current methods for collecting cloud data mainly rely on manual observation. This paper presents a novel approach to identifying ground-based cloud images to aid in the collection of cloud data. However, there is currently no publicly available dataset that is suitable for this research. To solve this, we built a dataset of surface-shot images of clouds called the SSC, which was overseen by the Macao Meteorological Society. Compared to previous datasets, the SSC dataset offers a more balanced distribution of data samples across various cloud genera and provides a more precise classification of cloud genera. This paper presents a method for identifying cloud genera based on cloud texture, using convolutional neural networks. To extract cloud texture effectively, we apply Gamma Correction to the images. The experiments were conducted on the SSC dataset. The results show that the proposed model performs well in identifying 10 cloud genera, achieving an accuracy rate of 80% for the top three possibilities.
Design of a Lightweight Cryptographic Scheme for Resource-Constrained Internet of Things Devices

We propose an ultra-lightweight cryptographic scheme called “Small Lightweight Cryptographic Algorithm (SLA)”. The SLA relies on substitution–permutation network (SPN). It utilizes 64-bit plaintext and supports a key length of 80/128-bits. The SLA cipher includes nonlinear layers, XOR operations, and round permutation layers. The S-box serves to introduce nonlinearity in the entire scheme design. It plays a vital role in increasing the complexity and robustness of the design. The S-box can thwart attacks such as linear and differential attacks. The scheme makes it possible to breed many active S-boxes in a short number of rounds, hindering analytical attacks on the cipher. When compared to other currently used ciphers, SLA has a higher throughput. Additionally, we demonstrate the SLA’s performance as an ultra-lightweight compact cipher, and its security analysis. The SLA cipher’s design is well suited for applications where small-scale embedded system dissipation is critical. The SLA algorithm is implemented using Python.
BDTA: events classification in table tennis sport using scaled-YOLOv4 framework

Computer vision algorithms based on deep learning have evolved to play a major role in sports analytics. Nevertheless, in sports like table tennis, detecting the ball is a challenge as the ball travels at a high velocity. However, the events in table tennis games can be detected and classified by obtaining the locations of the ball. Therefore, existing methodologies predict the trajectories of the ball but do not detect and classify the in-game events. This paper, therefore, proposes a ball detection and trajectory analysis (BDTA) approach to detect the location of the ball and predict the trajectory to classify events in a table tennis game. The proposed methodology is composed of two parts: i) Scaled-YOLOv4 which can detect the precise position of the ball ii) Analysis of trajectory based on ball coordinates to detect and classify the events. The dataset was prepared and labeled as a ball after enhancing the frame resolution with a super-resolution technique to get the accurate position of the ball. The proposed approach demonstrates 97.8% precision and 98.1% f1-score in detecting the location of the ball and 97.47% precision and achieved 97.8% f-score in classifying in-game events.