Message Length Research Articles

Low Latency Energy-Efficient Neural Decoders for Block Codes

Neural decoders are being explored for 5G and beyond communication networks which have ultra-low-latency and energy-efficiency requirements. We consider several ultra-short linear and non-linear block codes and design neural decoders for them, which (i) provide near maximum-likelihood (ML) performance in terms of bit and block error rates, and (ii) have low decoding complexity (and hence, latency and energy consumption) in terms of the number of hidden layers and hidden nodes. We explore (i) single-label, (ii) multi-label, and (iii) concatenated coding based decoders. We find that single-label decoders with a single hidden layer having a sufficient number of nodes achieve near-ML performance. Since the number of output nodes required in single-label decoders is exponential in the message length, we investigate a multi-label neural decoder requiring a significantly lower number of output nodes but having decreased performance in terms of block error rate. To eliminate extra errors, we concatenate an outer code with the original code as the inner code. The inner and outer codes are decoded by multi-label and single-label neural decoders, respectively, leading to better block error performance than that is achievable using multi-label decoders.

Minimal Effect of Messaging on Engagement in a Digital Anxiety Intervention.

This study evaluated the effectiveness of different recruitment messages for encouraging enrollment in a digital mental health intervention (DMHI) for anxiety among 1,600 anxious patients in a large healthcare system. Patients were randomly assigned to receive a standard message, or one of five messages designed to encourage enrollment: Three messages offered varying financial incentives, one message offered coaching, and one message provided consumer testimonials. Patients could then click a link in the message to visit the DMHI website, enroll, and start the first session. We examined the effects of message features and message length (short vs. long) on rates of site clicks, enrollment, and starting the first session. We also tested whether demographic and clinical factors derived from patients' electronic health records were associated with rates of enrollment and starting the first session to understand the characteristics of patients most likely to use DMHIs in this setting. Across messages, 19.4% of patients clicked a link to visit the DMHI website, but none of the messages were significantly associated with rates of site clicks, enrollment, or starting the first session. Females (vs. males) had a greater probability of enrollment. No other demographic or clinical variables were significantly associated with enrollment or starting the first session. Findings provide guidance for resource allocation decisions in larger scale DMHI implementations in healthcare settings.

An adaptive image steganography algorithm based on the use of non-cryptographic hash functions for data extraction

An adaptive steganography algorithm for data hiding in full-color images based on iterative introduction of minor distortions into blocks of container images and the use of high-speed non-cryptographic hash functions for data extraction is considered. Modification of the minimum number of container elements compared to the length of the hidden message is a distinctive feature of the algorithm. This feature allows the hidden throughput to be increased and the visual and statistical visibility of hidden data to be reduced. The algorithm is compared with modern algorithms of adaptive spatial steganography in terms of assessing the level of distorting changes in stego-containers. In addition, a modified version of the algorithm that implements covert channel multiplexing using a common subset of container elements when embedding various messages into them is considered.

Spam SMS Prediction Using Machine Learning

Abstract: As the popularity of mobile phone devices has increased, Short Message Service (SMS) has grown into a multi-billion dollars industry. At the same time, reduction in the cost of messaging services has resulted in growth in unsolicited commercial advertisements (spams) being sent to mobile phones. In parts of Asia, up to 30% of text messages were spam in 2012. Lack of real databases for SMS spams, short length of messages and limited features, and their informal language are the factors that may cause the established email filtering algorithms to underperform in their classification. In this project, a dataset of real SMS Spams from UCI Machine Learning repository is used, and after pre-processing and vectorization, different machine learning algorithms are applied to the dataset. Finally, the results are compared and the best algorithm for spam filtering for text messaging is introduced and converted into an open-source website. The SMS spam collection set is used for testing the method. After collecting the various supervised learning algorithms, we find that the Multinomial Naïve Bayes algorithm gives us 97.1% Accuracy and 100% Precision

Securing LSB steganography using bite substitution and image blocking

Protecting secret messages is a vital issue, in this paper’s research, a simplified, highly secure method of message steganography will be introduced. The proposed method will use a complicated PK, which contains information to select a secret block from the color image to be used as a covering block; also it will contain the values of the chaotic logistic map model to run this model to generate the indices key needed to substitute the message binary matrix. The PK will provide a huge keyspace capable to resist hacking attacks, the extracted message will be very sensitive to any minor changes in the PK, and any changes in this key during the extraction phase will be considered a hacking attempt by producing a damaged extracted message. It will be shown that the proposed method will be always efficient when changing the message length and changing the covering images. The proposed method will be implemented using various messages and various covering images, the obtained results will be analyzed using various types of data analysis methods to prove the improvements provided by the proposed method (quality, security, and efficiency).

Double layer steganography technique using DNA sequences and images.

Information security has become increasingly challenging as a result of massive advancements in information and communication technologies. Due to the necessity of exchanging private information and the open nature of the network, there is an increased risk of various types of attacks. Consequently, data security is an essential component of data communication. One of the most effective methods used to achieve secrecy is steganography. This method hides data within a cover object without raising suspicion. The level of security is improved when two steganography methods are combined. This approach is known as multilevel steganography, which hides sensitive data in two cover objects in order to provide a two-level security system. Accordingly, we developed a technique that focuses on protecting secrecy while also being robust to attacks. The new technique uses a multi-layer steganography mechanism by using DNA sequences and images as carriers for sensitive data. The technique intends to hide secret messages in the DNA using the substation algorithm, and then the fake DNA is embedded in an image utilizing the discrete cosine transform (DCT) method. Eventually, the stego image is sent to the intended recipient. Different types of images with different sizes and lengths of messages and DNA sequences were used during the experiments. The results show that the proposed mechanism is resistant to histogram and chi-square attacks. The maximum mean value observed was 0.05, which means the histograms of the original and stego images are nearly identical, and the stego image does not raise any suspicion regarding the existence of secret information. In addition, the imperceptibility ratios were good, as the highest PSNR and MSE values were 0.078 and 72.2, respectively. Finally, the PNG and BMP images show excellent results. On the other hand, the JPG images failed to meet the expected ratio of imperceptibility and security.

Analysis of the Cryptographic Algorithms in IoT Communications

The advances on recent communication paradigms brings new security and privacy challenges, mainly about personal data collection by smart devices. Resource constrained devices face serious issues to run complex cryptographic algorithms. Trying to evaluate the performance impact of those algorithms in usual and common devices used in smart homes, in this paper we tested the impact of different cryptographic algorithms in low computational devices, typically used in smart devices applied in smart homes, testing different security configurations and using the two most used transport protocols (HTTP and MQTT). The experiment measures their effects on six commonly used embedded devices in IoT WSNs: ESP8622, ESP32, and Raspberry Pi (RPi) from 1 to 4. The experiment measured the power consumption, message delay, and additional message length (bytes). Moreover, the analysis was also used to model security algorithms. The experimental results from long runs (72 hours) reveal the cryptographic solution choice is significant for the message delay and additional message length.

Increasing Efficiency in Addressing Patient Portal Messaging Demands on Healthcare Systems (P6-7.006)

<h3>Objective:</h3> To evaluate healthcare value of messaging through patient portals based on the relationship of maximum patient message length versus content communicated. <h3>Background:</h3> Patient portals are designed to empower individuals to be partners in their care and reduce administrative burdens. Overall adoption has been positive with studies demonstrating improvement in outcomes with increased use. However, recent studies have shown that high volume of messages increases physician workload and contributes to burnout. Specifically, length of the message has been described as a contributing factor. Here we evaluate a database of messages to determine key descriptive statistics towards identifying methods to increase efficiency in addressing messaging demands. <h3>Design/Methods:</h3> Retrospective review of patient portal messages through the Epic MyChart platform from January 1 to June 30, 2022 at a single large academic outpatient neurology clinic (general &amp; subspecialities). Message character length was obtained through a basic character count using Microsoft Excel (MAC version 16.65). Descriptive statistics were calculated from the sample to derive insights on patterns. Through an optimization scenario - maximum message length was reduced in order to determine number of messages affected versus content reduced. <h3>Results:</h3> There were 10,118 messages from 2,646 patients. Length ranged from 1 to 1500 characters resulting in a total of 2,946,290 characters. Message length followed a decreasing exponential distribution, with majority of messages in lower character ranges. Quartiles from first to fourth were: 88, 202 (median), 386, and 1500 (max). Limiting character count to 750 characters, affected 807 (8%) messages. Reducing these messages to 750 characters decreased the overall character count by 11%. <h3>Conclusions:</h3> Patient portal messaging has become an important part of the patient-physician partnership. This analysis evaluated an optimization strategy of reducing character count in order to determine workload impact. Limiting maximum length to 750 characters led to a disproportionate reduction in workload compared to a smaller number of messages affected. <b>Disclosure:</b> An immediate family member of Dr. Gordon-Kundu has received personal compensation for serving as an employee of Ceretype . An immediate family member of Dr. Gordon-Kundu has received personal compensation in the range of $0-$499 for serving as a Consultant for Hyperfine. An immediate family member of Dr. Gordon-Kundu has stock in Hyperfine. Dr. Khokhar has nothing to disclose.

Investigating the Role of Context in the Delivery of Text Messages for Supporting Psychological Wellbeing.

Without a nuanced understanding of users' perspectives and contexts, text messaging tools for supporting psychological wellbeing risk delivering interventions that are mismatched to users' dynamic needs. We investigated the contextual factors that influence young adults' day-to-day experiences when interacting with such tools. Through interviews and focus group discussions with 36 participants, we identified that people's daily schedules and affective states were dominant factors that shape their messaging preferences. We developed two messaging dialogues centered around these factors, which we deployed to 42 participants to test and extend our initial understanding of users' needs. Across both studies, participants provided diverse opinions of how they could be best supported by messages, particularly around when to engage users in more passive versus active ways. They also proposed ways of adjusting message length and content during periods of low mood. Our findings provide design implications and opportunities for context-aware mental health management systems.

Driver Understanding and Secondary Task Performance While Viewing Traffic Safety Messages on Dynamic Message Signs

Many agencies use dynamic message signs to display behavioral traffic safety (BTS) messages (e.g., CLICK IT OR TICKET, DONT DRINK AND DRIVE). However, little guidance exists on how best to design and display such messages on those signs. In this paper, the results of a human factors laptop-based study are described, which examined how well drivers can read and interpret BTS messages while simultaneously attending to a secondary control task similar to how drivers must operate their vehicle while simultaneously reading road signs. The study results indicated that displaying BTS messages that include humor, wit, or pop culture references could have adverse consequences on driving behavior for motorists who are unable to correctly interpret the safety topic of those messages. The results also indicated that BTS messages should only address a single safety topic. The length of the BTS message should be kept to 16 words or fewer in low to moderate driver workload environments; in high driver workload environments, BTS messages should be limited to 11 words or fewer.

Subsequent Embedding in Targeted Image Steganalysis: Theoretical Framework and Practical Applications

Steganalysis is a collection of techniques used to detect whether secret information is embedded in a carrier using steganography. Most of the existing steganalytic methods are based on machine learning, which requires training a classifier with “laboratory” data. However, applying machine-learning classification to a new data source is challenging, since there is typically a mismatch between the training and the testing sets. In addition, other sources of uncertainty affect the steganlytic process, including the mismatch between the targeted and the actual steganographic algorithms, unknown parameters –such as the message length– and having a mixture of several algorithms and parameters, which would constitute a realistic scenario. This article presents subsequent embedding as a valuable strategy that can be incorporated into modern steganalysis. Although this solution has been applied in previous works, a theoretical basis for this strategy was missing. Here, we cover this research gap by introducing the “directionality” property of features concerning data embedding. Once a consistent theoretical framework sustains this strategy, new practical applications are also described and tested against standard steganography, moving steganalysis closer to real-world conditions.

Sharing unwanted sexual experiences online: A cross-platform analysis of disclosures before, during and after the #MeToo movement

Online disclosure of sexual violence victimisation is a relatively new phenomenon. While prior research has mainly relied on analysis of Twitter data from the #MeToo period, this study compares such disclosures across platforms over two years. Using machine learning, 2927 disclosures were identified for quantitative content analysis and multiple correspondence analysis. Online platforms differed in timing of the posts, information shared, information density, co-occurrence of information and the length of the disclosure message. Most disclosures were found on the platform Twitter, and during the #MeToo movement. These posts differ from disclosures on other platforms and outside the viral movement. Regarding the content, across all platforms and periods, clustering was found around offender-oriented information, making the offender an explicit part of the experience. This study shows that an exclusive focus on online disclosures on Twitter and during viral movements gives a biased and incomplete picture of what online disclosure of sexual victimisation entails. Our cross-platform analysis over time allows for more universal statements about the content and context of online disclosures of sexual victimisation.

Improving the Use of Chaotic Keys in Message Cryptography

Using chaotic logistic keys in message cryptography is a popular technique; it provides a high level of message security by protecting it from being hacked. This technique becomes inefficient when the message length is big, because a chaotic key of big length requires much time to be generated. To overcome this problem this paper research will provide a novel method for short and long messages cryptography. The new method will use a complicated private key which will provide a huge key space to resist any kids of attacks. The private key will be used to divide the message into equal blocks (except the last block), and it will be also used to generate the index keys which are required to apply encryption and decryption, these keys will be generated from the obtained chaotic logistic keys. The proposed method will use a simple encryption and decryption functions, these functions will use the index keys values to distribute the source message to form the encrypted message and to retrieve the decrypted message from the encrypted one. A recommendation of message dividing into blocks will be suggested, depending on the message length: when to divide a message and how many blocks are required. The required number of message blocks will be tested for various in length messages, the throughputs will be calculated to justify the process of message dividing into blocks. The proposed method will tested using various short and long messages, the obtained results will be analyzed to prove the quality, security, sensitivity and the efficiency of the proposed method.

Hierarchical clinical decision support for breast cancer care empowered with Bayesian networks

PurposeBreast cancer is a global public health dilemma and the most prevalent cancer in the world. Effective treatment plans improve patient survival rates and well-being. The five-year survival rate is often used to develop treatment selection and survival prediction models. However, unlike other types of cancer, breast cancer patients can have long survival rates. Therefore, the authors propose a novel two-level framework to provide clinical decision support for treatment selection contingent on survival prediction.Design/methodology/approachThe first level classifies patients into different survival periods using machine learning algorithms. The second level has two models with different survival rates (five-year and ten-year). Thus, based on the classification results of the first level, the authors employed Bayesian networks (BNs) to infer the effect of treatment on survival in the second level.FindingsThe authors validated the proposed approach with electronic health record data from the TriNetX Research Network. For the first level, the authors obtained 85% accuracy in survival classification. For the second level, the authors found that the topology of BNs using Causal Minimum Message Length had the highest accuracy and area under the ROC curve for both models. Notably, treatment selection substantially impacted survival rates, implying the two-level approach better aided clinical decision support on treatment selection.Originality/valueThe authors have developed a reference tool for medical practitioners that supports treatment decisions and patient education to identify patient treatment preferences and to enhance patient healthcare.

A Survey on Anonymous Communication Systems With a Focus on Dining Cryptographers Networks

Traffic analysis attacks can counteract end-to-end encryption and use leaked communication metadata to reveal information about communicating parties. With an ever-increasing amount of traffic by an ever-increasing number of networked devices, communication privacy is undermined. Therefore, Anonymous Communication Systems (ACSs) are proposed to hide the relationship between transmitted messages and their senders and receivers, providing privacy properties known as anonymity, unlinkability, and unobservability. This article aims to review research in the ACSs field, focusing on Dining Cryptographers Networks (DCNs). The DCN-based methods are information-theoretically secure and thus provide unconditional unobservability guarantees. Their adoption for anonymous communications was initially hindered because their computational and communication overhead was deemed significant at that time, and scalability problems occurred. However, more recent contributions, such as the possibility to transmit messages of arbitrary length, efficient disruption handling and overhead improvements, have made the integration of modern DCN-based methods more realistic. In addition, the literature does not follow a common definition for privacy properties, making it hard to compare the approaches’ gains. Therefore, this survey contributes to introducing a harmonized terminology for ACS privacy properties, then presents an overview of the underlying principles of ACSs, in particular, DCN-based methods, and finally, investigates their alignment with the new harmonized privacy terminologies. Previous surveys did not cover the most recent research advances in the ACS area or focus on DCN-based methods. Our comprehensive investigation closes this gap by providing visual maps to highlight privacy properties and discussing the most promising ideas for making DCNs applicable in resource-constrained environments.

Hardware implementations of LBlock and XXTEA lightweight block ciphers for resource-constrained IoT application

Recent growth in the number of connected Internet of Things (IoT) devices in a network has raised lot of security related issues. Since these devices are mostly battery powered, have low memory and weak computational capability, therefore lightweight ciphers are the most suitable choice for providing security. Among various lightweight ciphers available, we have chosen to implement corrected block TEA (XXTEA) and LBlock ciphers. These lightweight ciphers are feistel based, having simple encryption algorithms. In this work, two different architectures are proposed namely round-based implementation of LBlock cipher and serial implementation of XXTEA cipher with variable length message. Both the architectures are implemented on different field programmable gate array (FPGA) device families and application-specific integrated circuit (ASIC) implementation is performed on 0.18 μm complementary metal-oxide semiconductor (CMOS) technology. By analysing the performance metrics, comparison of the proposed work is done with the existing ciphers. For the proposed LBlock and XXTEA variable length architectures, the percentage improvement obtained with respect to area consumption is 50.92% and 26.04% respectively.

Multi-Agent Reinforcement Learning Based on Representational Communication for Large-Scale Traffic Signal Control

Traffic signal control (TSC) is a challenging problem within intelligent transportation systems and has been tackled using multi-agent reinforcement learning (MARL). While centralized approaches are often infeasible for large-scale TSC problems, decentralized approaches provide scalability but introduce new challenges, such as partial observability. Communication plays a critical role in decentralized MARL, as agents must learn to exchange information using messages to better understand the system and achieve effective coordination. Deep MARL has been used to enable inter-agent communication by learning communication protocols in a differentiable manner. However, many deep MARL communication frameworks proposed for TSC allow agents to communicate with all other agents at all times, which can add to the existing noise in the system and degrade overall performance. In this study, we propose a communication-based MARL framework for large-scale TSC. Our framework allows each agent to learn a communication policy that dictates “which” part of the message is sent “to whom”. In essence, our framework enables agents to selectively choose the recipients of their messages and exchange variable length messages with them. This results in a decentralized and flexible communication mechanism in which agents can effectively use the communication channel only when necessary. We designed two networks, a synthetic <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4 \times 4$ </tex-math></inline-formula> grid network and a real-world network based on the Pasubio neighborhood in Bologna. Our framework achieved the lowest network congestion compared to related methods, with agents utilizing <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sim 47-65 \%$ </tex-math></inline-formula> of the communication channel. Ablation studies further demonstrated the effectiveness of the communication policies learned within our framework.

Proposed Approach for Key Generation Based on the RNA

Due to fundamental role of random key in the design of cryptography algorithms, a new method is proposed for generating random key based on the bases of RNA translation to protein chain. The proposed method accept a key sequence size(9,18,27,…) byte and generate key sequence with extended length appropriate with plain message length. The security strength of generated key is acceptable according to the results of statistical tests of randomness

Does Length Matter? The Impact of Fact-Check Length in Reducing COVID-19 Vaccine Misinformation

ABSTRACT This study examines the impact of message length and audience’s perceived information overload on the effectiveness of a fact-check in reducing belief in fake news within the COVID-19 vaccination context. Through an online experiment (N = 374) conducted in Singapore, we found an interaction effect between one’s level of information overload and the type of fact-check that was being shown (short, medium, or long). The findings from this study help to extend the literature on the effectiveness of fact-checks in reducing general public’s belief in fake news.

SCB Mode: Semantically Secure Length-Preserving Encryption

To achieve semantic security, symmetric encryption schemes classically require ciphertext expansion. In this paper we provide a means to achieve semantic security while preserving the length of messages at the cost of mildly sacrificing correctness. Concretely, we propose a new scheme that can be interpreted as a secure alternative to (or wrapper around) plain Electronic Codebook (ECB) mode of encryption, and for this reason we name it Secure Codebook (SCB). Our scheme is the first length-preserving encryption scheme to effectively achieve semantic security.