Message Length Research Articles

Effects of Varying Text Message Length and Driving Speed on the Disruptive Effects of Texting on Driving Simulator Performance: Differential Effects on Eye Glance Measures

Eye glance analysis and driving performance during texting while driving: Differential effects of varying driving speed versus text message length. Background and Objective. Texting while driving continues to be a significant public health concern. Eye glances off the roadway are a measure of the visual distraction associated with texting while driving. In the present study, we examined the effects of two ‘real-world’ factors relating to the adverse effects of texting on driving performance and eye glances off the roadway: (1) text message length and (2) driving speed. Methods. Subjects ‘drove’ a fixed-base simulator and read, typed and sent text messages while driving. In study #1, the driving speed was 60 mph and the effects of short (1 word) versus longer (8–10 words) texts were compared. In study #2, the text messages were short only and driving speed was 60 or 80 mph. Driving performance was assessed using the Standard Deviation of Lane Position (SDLP). Video recordings of the drivers’ faces were used to assess eye glances from the road to the phone—and back—during texting. Results. Texting while driving impaired driving performance as measured by SDLP, and both longer text messages and faster drive speeds made driving performance even worse. Analysis of the eye glance data, however, revealed different effects of these two manipulations. Specifically, longer text messages were associated with an increase in the number of eye glances to the phone during a text message episode, an increase in the total time spent with the eyes off the road, and an increase in the single longest eye glance from the road. Moreover, with longer text messages the longest single eye glance away from the road typically occurred at or near the end of the text message episode. In contrast, increasing driving speed to 80 mph did not affect any of these eye glance measures relative to driving at 60 mph. Conclusion and Application. Both text message length and driving speed while texting adversely affect driving performance, but they do so via different mechanisms. These results have implications for how to tailor “don’t text and drive” messaging to better serve the public health.

Stealthy Messaging: Leveraging Message Queuing Telemetry Transport for Covert Communication Channels

Covert channel methods are techniques for improving privacy and security in network communications. These methods consist of embedding secret data within normal network channels, making it more difficult for unauthorized parties to detect such data. This paper presents a new approach for creating covert channels using the Message Queuing Telemetry Transport (MQTT) protocol, widely used in the context of the Internet of Things (IoT). The proposed method exploits storage channels by altering the field length of MQTT messages. Our solution leverages well-known one-way mathematical functions to ensure that data remain hidden from third parties observing the MQTT stream. In this way, we ensure that not only the content of the communication is preserved but also that the communication itself takes place. We conducted a security analysis to show that our solution offers the above-mentioned property even against severe threats, such as an adversary being able to observe all the messages exchanged in the network (even in the clear). Finally, we conducted an overhead analysis of our solution both in terms of the time required to perform the required operations and of the bytes to send. Our study shows that our solution adds no significant time overhead, and the additional overhead in terms of transmitted bytes remains within acceptable limits.

The Effect of Anti-Tobacco Ad Length and Skippability on Perceived Message Effectiveness and Emotional Responsiveness

ABSTRACT The media landscape changes have resulted in significantly more people viewing content in shorter and skippable formats. There is limited evidence about the impact of these formats on anti-tobacco campaign effectiveness. This study examined the effect of anti-tobacco ad length and skippability on perceived message effectiveness (PME) and emotional responsiveness among people who smoke. Initially, 805 people who smoke were randomized to a message type (anti-tobacco vs. control) × message length (15 vs. 30 seconds) condition. A week later, participants saw the same message, but were randomized to a skippable or non-skippable version. Following ad exposure, outcomes were measured and examined in mixed-effects linear regression models. We found that at initial exposure, participants in the 30-second anti-tobacco condition reported significantly higher scores on personal relevance (75% cf. 68%), motivation to act (61% cf. 52%), action-oriented negative emotion (62% cf. 53%) and positive emotion (37% cf. 28%) relative to participants in the 15-second anti-tobacco condition. At repeated exposure, there was no effect of ad skippability on PME or emotional responsiveness regardless of message type condition. However, participants in the 30-second anti-tobacco condition reported significantly higher scores on action-oriented negative emotion (60% cf. 50%) and inaction-oriented negative emotion (54% cf. 46%) relative to participants in the 15-second anti-tobacco condition. Our findings suggest that initial exposure to non-skippable and longer novel anti-tobacco messages can lead to stronger personal relevance, motivation to act and emotional arousal, and ongoing negative emotional arousal, which may attenuate the negative effects of allowing individuals to skip later.

Implementation of Least Significant Bit Steganography to Secure Text Messages in Images

Steganography is a technique of securing secret messages in other messages that are not known. Simulation of the steganography method using the Least Significant Bit technique is used to change the last bit in one byte of data by using a text message as the container medium. This study aims to implement the security of text messages in images using the Least Significant Bit technique which is supported by the steganography method. Simulation techniques are used to conduct studies using Cryptool2 which can describe the concept of cryptography. The results obtained from this study regarding the security of text message insertion into an image in *.jpg and *.png format with 5 sampling trials are (1) the encrypted image cannot be distinguished directly through human eyes, (2) there is an increase in file size the image after being encrypted with an average for five trials is 0.31%, this increase depends on the length of the text message and a key to be inserted, the longer the insertion, the larger the resulting file size, (3) The higher the resolution of the image where the description encryption is inserted, the longer the process required, (4) The simulation time of steganographic decryption is faster than steganographic encryption. The decryption simulation process is the same as 50% of the encryption process.

Optimization analysis of average message delivery time for healthcare monitoring using a developed NB-IoT technology in a smart city

In contrast to prior research that lacked a comprehensive analysis of a fundamental parameter, namely the average message delivery time (AMDT), this study significantly contributes to the academic community by offering cutting-edge insights into AMDT analysis for wireless healthcare monitoring in smart cities. Employing the innovative Narrowband Internet of Things (NB-IoT) technology, this article thoroughly investigates AMDT within wireless healthcare monitoring. The study focuses on analyzing AMDT during the transmission and reception of messages from base stations (BS) to remote healthcare data aggregators (HDAs) serving remote patients (RPs). Various vital parameters are considered, including modulation rate, average message length (AML), the average number of hops per path (H) required to reach remote patients, patient count, sub-carrier spacing (SS), and the modulation type within the communication range of the BS. This proposed assessment of health network performance offers a brief overview based on essential criteria, facilitating swift and precise evaluations of the efficacy of mobile health applications. The results obtained and the in-depth discussion of the outcomes of AMDT analysis provide valuable insights that can be harnessed to enhance the design and operation of wireless healthcare monitoring systems.

Fault probability identification method for distribution networks based on mov-MF distribution

To address the fault identification challenge in distribution networks, a method leveraging a mixture of the von Mises–Fisher (mov-MF) distribution model for fault probability identification is proposed. Initially, the synchronous phasor measuring unit is employed to gather the post-fault steady-state voltage phase quantities, and then, the voltage phase angle values are combined to form a three-dimensional feature quantity. Subsequently, the mov-MF distribution model is initialized through the spherical K-means algorithm and the minimum message length algorithm. This model is further refined via the expectation–maximization algorithm to iteratively optimize distribution parameters. The test set data are input into the mov-MF distribution model, which has been constructed using typical fault data, to discern fault types. Finally, the efficacy of the proposed method is validated through simulation verification conducted on the IEEE 33-node distribution system. The analysis of the examples demonstrates the accuracy of the mov-MF distribution model-based fault identification method in identifying single-phase ground, two-phase ground, two-phase interphase, and three-phase short-circuit faults.

An Efficient Lossless Compression Algorithm for Maritime Safety Information Using Byte Encoding Network

The short message function of the BeiDou satellite system, due to its strong concurrent processing capabilities, can quickly and accurately send information to the target location in emergency situations. However, because of data redundancy and limitations on message length, a single piece of information often requires multiple transmissions through BeiDou short messages to be completed, resulting in limited transmission capacity. To improve the transmission capacity of information, it is necessary to compress the information during transmission using BeiDou’s short message communication function for maritime safety information. This paper proposes a Byte Encoding-enhanced Prediction by Partial Matching, variant D (BPPMd) algorithm that is particularly suitable for transmitting maritime safety information. Combined with a maritime safety information encoding algorithm (ME), it further improves compression efficiency, optimizes byte space, reduces information redundancy, and ensures the accuracy of the information. In this study, we constructed a maritime safety information dataset that includes three categories of information: meteorological warnings, navigation warnings, and disaster warnings. Experimental results show that the proposed algorithm is particularly suitable for compressing the maritime safety information dataset and outperforms other benchmark algorithms. Therefore, this study indicates that the proposed lossless compression method can be a feasible and effective solution for BeiDou short message communication.

Explainable finite mixture of mixtures of bounded asymmetric generalized Gaussian and Uniform distributions learning for energy demand management

We introduce a mixture of mixtures of bounded asymmetric generalized Gaussian and uniform distributions. Based on this framework, we propose model-based classification and model-based clustering algorithms. We develop an objective function for the minimum message length (MML) model selection criterion to discover the optimal number of clusters for the unsupervised approach of our proposed model. Given the crucial attention received by Explainable AI (XAI) in recent years, we introduce a method to interpret the predictions obtained from the proposed model in both learning settings by defining their boundaries in terms of the crucial features. Integrating Explainability within our proposed algorithm increases the credibility of the algorithm’s predictions since it would be explainable to the user’s perspective through simple If-Then statements using a small binary decision tree. In this paper, the proposed algorithm proves its reliability and superiority to several state-of-the-art machine learning algorithms within the following real-world applications: fault detection and diagnosis (FDD) in chillers, occupancy estimation and categorization of residential energy consumers.

Do 360-Character Wireless Emergency Alert (WEA) Messages Work Better than 90-Character Messages? Testing the Risk Communication Consensus.

Based on early evidence, risk communication scholars have come to believe that longer (360-character maximum) mobile public warning messages generate more compliance than shorter (90-character maximum) messages. This study used an experimental design to test that premise. The study measured participants' (N = 481) likelihood of compliance in response to a mock Wireless Emergency Alert (WEA) message, as well as alternatives to immediate compliance: seeking additional information, taking non-recommended action, or ignoring the message. The study found that both longer and shorter messages generated relatively high levels of compliance, but longer messages did not generate higher levels of compliance. Rather than message length, risk personalization and hazard experience were stronger differentiators of WEA message response outcomes. Results included a moderation effect: Shorter messages produced slightly greater compliance than longer messages among people who reported lower levels of risk personalization. The study concluded that 90-character messages may be more effective than previously believed. Consequently, the authors recommend renewed focus on public safety communication related to risk personalization and hazard experience. (169 words).

Data & Semantic Analysis Fake News Detection Based Domain-Specific Auto Encoder Learning

Abstract: Efforts to create technology for automatically detecting fake news are actively underway as the spread of misinformation on social media continues to escalate. However, most of these approaches primarily concentrate on the linguistic and structural aspects of fake news, such as identifying sources or authors, message length, and the frequency of negative language. In contrast, our study introduces a phony news detection model leveraging machine learning that incorporates user behaviors, news, and social capital-based social network dynamics. We used the XG Boost model to evaluate each feature's significance and identify the critical elements affecting the identification of fake news to capture the variables related to the spread of phony news entirely. We implemented SVM, RF, LR, CART, and NNET, well-known machine learning classification models, using these identified factors and assessed how well they detected bogus news. This work used a cross-validation process to prevent overfitting and to generalize the established models. Additionally, the predicted accuracy of the established models was compared. The RF model had the best forecast accuracy, about 94%, while the NNET had the worst performance rate, about 92.1%. As disinformation is created and disseminated with growing sophistication, the results of this study should improve the effectiveness of false news detection systems.

Enhancement of L. Voleti LSB technique applied to Image Steganography

In today’s world, the progress of communication technology plays a role in our everyday lives. The internet and the conversion of data into forms have significantly expanded the sharing of information leading to fast-paced growth. Despite having secure methods available, safeguarding data remains a top priority. This study aims to improve the security and durability of data transmission by combining steganography and cryptography techniques. Steganography, which involves hiding messages within another medium, and cryptography which entails encrypting messages using keys are both effective ways to ensure data security. The research delves into combinations of algorithms that integrate steganography and cryptography examining their impact on enhancing security. It categorizes methods based on technical and non-technical aspects as well as their domains. Moreover, it underscores the importance of data security in media formats such as images, videos, and audio. In analyzing message lengths noticeable character patterns were observed in both Vigenère Cipher and Elliptic Curve Cryptography. In short messages "s" was prominent in the Vigenère Cipher accounting for 26.3% of occurrences while Elliptic Curve Cryptography showed a prevalence of "t" at 3.9%. Medium-length messages continued to show the dominance of "s" in the Vigenère Cipher at 22.5% while Elliptic Curve Cryptography highlighted "p" as a character at 4.3%. Lastly, longer messages maintained the prevalence of "s" in the Vigenère Cipher at 24.3% with "v" emerging as dominant in Elliptic Curve Cryptography at 3.9%. These findings show that combining steganography and cryptography methods can greatly enhance the protection of multimedia data.

An Improved Method for Detecting Covert Channels in the Transport Layer

Steganography is one of the techniques used both to leak information and to prevent theft and distortion of confidential information. Covert channels in the network are one of the platforms for information steganography in the network. Often, this method is realized using the network's protocols. Network protocols are used as an information transfer medium. Network protocols can contain critical information and carry them into the network without attracting consideration. Covert channels are evaluated with three different criteria including capacity, robustness, and insensitivity. Also, the methods to deal with it include removing, limiting, and diagnosing the channel. By definition, covert channels are used to realize covert communication. Steganography methods are safe if the stego has no detectable signatures. Put differently, the stego statistical properties (audio, image, and video) should be similar to the cover properties. The ability to discover the message in the stego depends on the length of the hidden message. The proposed method in this study is to consider the amount of redundant information in a repository, explore the discoverable signs of the covert channel, and check the packet length in the checksum field in the UDP protocol at the transmission layer.

Design and analysis of stochastic 5G new radio LDPC decoder using adaptive sparse quantization kernel least mean square algorithm for optical satellite communications

AbstractA Stochastic Low‐Density Parity‐Check (LDPC) decoder is a type of 5G New Radio standard LDPC decoder that uses stochastic techniques to perform decoding. Stochastic LDPC decoding with 5G NR standard typically uses an iterative process, where messages exchanged among variable nodes (VN), check nodes multiple times. Stochastic LDPC decoders are often used in scenarios where the received signal is subject to varying levels of noise. They will provide improved error correction performance compared to traditional LDPC decoders, especially when dealing with channels with varying signal‐to‐noise ratios in 5G networks. Using the adaptive sparse quantization kernel least mean square algorithm (SLDPC‐ASQ‐KLMSA), this paper proposes an area‐efficient architecture design for a stochastic LDPC decoder. The LDPC code (2048, 1723) is taken from the LOGBASE‐T standard and used in this study. We examine the ASQ‐KLMSA connection effects. Starting with the VN. It makes checking node functioning easier and reduces inter‐connect complexity by capping extrinsic message length at 2 bits. Because of the simplified check node operation in ASQ‐KLMSA, the decoder nodes must exchange messages with a greater degree of accuracy. The 3–3 input grouping sub‐node of the degree‐6 VN was changed with an adder‐based 5–1 input grouping sub‐node for the (2048, 1723) code in order to get more accurate results when the check‐to‐variable messages aren't strong enough. A suggested decoder architecture was determined using a stochastic LDPC decoder developed for TSMC 65 nm process (2048, 1723). Bite error rate, throughput, mean square error, latency, power, and area usage are some of the metrics used to evaluate the effectiveness of the SLDPC‐ASQ‐KLMSA algorithm that has been suggested and implemented in Python. Thus, the proposed approach has attained 34.44%, and 38.39% low mean square error while compared with the existing methods such as higher‐performance stochastic LDPC decoder architecture designed through correlation analysis (HP‐SLDPC‐CA), Higher Throughput and Hardware Efficient Hybrid LDPC Decoder Utilizing Bit‐Serial Stochastic Updating(HLDPC‐BSSU), Flexible FPGA‐Based Stochastic Decoder for 5G LDPC codes (FPGA‐SD‐5G‐LDPC), respectively.

Enhancing secret key distribution through advanced color modulation in visible light communication

Visible light communication (VLC) has emerged as a dynamic area of research poised to revolutionize high-speed wireless communication. VLC technology uses light-emitting diodes (LEDs) within existing infrastructure to emit light within the visible spectrum. VLC complements traditional radio frequency (RF) communications, addressing its inherent limitations and drawbacks. To navigate the demands of modern urban environments, VLC systems must prioritize secure data transmission, accessibility, and economic feasibility, particularly within the framework of smart cities. We introduce what is to our knowledge a novel privacy-enhanced VLC system for optical wireless communication. Leveraging color data modulation techniques and the intricacies of a hyperchaotic three-dimensional map, this innovative approach ensures robust security. By employing diverse LED colors for data transmission and exploiting the unpredictable mathematical properties of hyperchaotic maps, enhanced privacy is achieved. The performance of the proposed system was rigorously evaluated through various tests, manipulating initial control parameters of the encryption process with the hyperchaotic map, as well as adjusting message length and content. Tests were conducted over a 1 m connection distance at a symbol transmission rate of 2 baud. Remarkably, the proposed system demonstrated high accuracy in message recovery, achieving a symbol error rate (SER) of only 0.02 at an incident optical power of 22 µW. We highlight the critical importance of precise decryption parameter values in the proposed method, demonstrating the necessity for accuracy within the range of 10−15 for each decryption parameter; it underscores the indispensability of meticulous parameter calibration to ensure the correct decryption of transmitted symbols. These results pave the way for applications where absolute security is imperative, particularly in smart city environments, such as for key distribution purposes.

A Lightweight Secure Scheme for Underwater Wireless Acoustic Network

Due to the open underwater channels and untransparent network deployment environments, underwater acoustic networks (UANs) are more vulnerable to hostile environments. Security research is also being conducted in cryptography, including authentication based on asymmetric algorithms and key distribution based on symmetric algorithms. In recent years, the advancement of quantum computing has made anti-quantum attacks an important issue in the field of security. Algorithms such as lattice and SPHINCS+ have become a research topic of interest in the field of security. However, within the past five years, few papers have discussed security algorithms for UANs to resist quantum attacks, especially through classical algorithms. Some existing classical asymmetric and symmetric algorithms are considered to have no prospects. From the perspective of easy deployment in engineering and anti-quantum attacks, our research focuses on a comprehensive lightweight security framework for data protection, authentication, and malicious node detection through the Elliptic Curve and Hash algorithms. Our mechanism is suitable for ad hoc scenarios with limited underwater resources. Meanwhile, we have designed a multi-party bit commitment to build a security framework for the system. A management scheme is designed by combining self-certifying with the threshold sharing algorithm. All schemes are designed based on certificate-less and ad hoc features. The proposed scheme ensures that the confidentiality, integrity, and authentication of the system are well considered. Moreover, the scheme is proven to be of unconditional security and immune to channel eavesdropping. The resource and delay issues are also taken into consideration. The simulations considered multiple variables like number of nodes, attackers, and message length to calculate proper values that can increase the efficiency of this scheme. The results in terms of delay, delivery ratio, and consumption demonstrate the suitability of the proposal in terms of security, especially for malicious node detection. Meanwhile, the computational cost has also been controlled at the millisecond level.

A hierarchical count data clustering based on Multinomial Nested Dirichlet Mixture using the Minorization-Maximization framework

Despite the widespread acceptance of mixture models among researchers, obtaining good results involves several challenges. In this paper, we address two main challenges: parameter estimation and data representation strategies.Expectation-Maximization (EM) is a widely used framework for parameter estimation. However, many factors complicate the process through intractable calculations of the posterior distribution and the parameters. Minorization-Maximization (MM) is an alternative framework that relaxes the complications and requirements of the EM. This paper adopts the MM framework for the Multinomial Nested Dirichlet Mixture in a hierarchical manner. The hierarchical nature of the MNDM is exploited through a Hierarchical Feature Learning framework (HFL), where the data represented is a result of the well-known Spatial Pyramid Matching method. Moreover, the mixture's components are determined by the Minimum Message Length (MML). Therefore, this paper presents an HFL framework for the data representation of the MNDM, where its learning is based on the MM framework, and its selection is based on MML. The validation of the two addressed improvements is proven through three visual datasets using recall and precision performance metrics.

Simultaneous count data feature selection and clustering using Multinomial Nested Dirichlet Mixture

The elevating effect of the curse of dimensionality in count data has made clustering a challenging task. This paper solves this by adopting the concept of feature saliency as a feature selection method in the context of using the Multinomial Nested Dirichlet Mixture (MNDM). The MNDM is a generalization of the Dirichlet Compound Mixture (DCM) that suffers from several limitations. The model learning is accomplished through the expectation-maximization method. The Minimum Message Length criterion is used to simultaneously determine the best number of components in the mixture with the updated selected features. At the price of convergence times, the results show better performance through different metrics, as the model aims to select the salient features and tune away the non-salient anomalistic features.

Text-based information design for in-vehicle displays: A systematic review

Reading short texts at a brief glance behaviour has been increasing, especially in the in-vehicle interface of the multitask context of driving. This behaviour led to a change in the approach of preparing and designing in-car information. Consequently, understanding the current status of existing design principles is necessary for designers to find the appropriate solution for text presentation on the in-vehicle display under driving conditions. Thus, the present study conducts a systematic review of text-based information design for in-vehicle displays. A total of 1,495 studies (1980–2022) were screened, and 360 emerged for full-text review; 27 studies met the inclusion criteria, including journal articles concerning the design of text-based information for in-vehicle displays involving text design and the effect on safe driving or reducing workload and distraction. The results were summarised in three themes of literature: existing design guidelines, design factors, and evaluation methodology. The existing guidelines and recommendations focus on the physical presence of text (modality, location, colour, typeface, font size), with few recommendations on the linguistic presence of the text (message style and message length). The discussion and future work section enables further work on developing usable and safe informational text displays on the in-vehicle interface and other short glance reading engagements.

New private information retrieval codes for imperfect privacy

In this paper, we introduce new codes for the private information retrieval (PIR). The conventional Tian-Sun-Chen (TSC) scheme achieves PIR capacity under the perfect privacy conditions with the minimum message length. Based on the TSC scheme, weakly PIR (WPIR) schemes have been developed to benefit download costs by relaxing privacy. We show that the proposed scheme has lower download cost than the TSC scheme in the weak privacy setting. While our scheme is not capacity-achieving under perfect privacy, it has smaller message sizes compared with the TSC scheme.

Entropically secure encryption with faster key expansion

Entropically secure encryption is a way to encrypt a large plaintext with a small key and still have information-theoretic security, thus in a certain sense circumventing Shannon’s result that perfect encryption requires the key to be at least as long as the entropy of the plaintext. Entropically secure encryption is possible when a lower bound is known on the entropy of the plaintext from the adversary’s point of view. The typical implementation is to expand the short key to the size of the plaintext, e.g. by multiplication with a public random string, and then use one-time pad encryption. This works in the classical as well as the quantum setting. In this paper, we introduce a new key expansion method that is faster than existing ones. We prove that it achieves the same security. The speed gain is most notable when the key length is a sizeable fraction of the message length. In particular, a factor of 2 is gained in the case of approximate randomization of quantum states. In the classical case, we obtain a reduction of the ciphertext size.