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The compressed code of Absolute Moment Block Truncation Coding (AMBTC) consists of quantized values (QVs) and bitmaps. The QVs exhibit greater predictability, and the bitmaps themselves carry more randomness. While existing joint coding and data hiding based methods have focused on compressing the QVs, none of them have addressed the coding of bitmaps. Furthermore, evidence also reveals that the sub-divided bitmaps exhibit a highly uneven pattern distribution. Therefore, we propose an efficient method to compress the bitmaps by representing sub-divided bitmaps as decimal digits. This exploits the varying frequency of certain digits, allowing Huffman encoding for shorter codewords to represent frequently occurring digits. Moreover, we have observed that less frequent digits, which require longer codewords, tend to appear among blocks with smaller difference of QVs. As a solution, we employ an adaptive approach to directly record the original bitmap when the difference is below a specific threshold. Otherwise, we employ Huffman encoding to reduce the code length. The experimental results demonstrate the effectiveness of our approach in reducing the storage space required for Lena's bitmaps by 7.36%. Moreover, the reduction in bitrate is more pronounced when the test image exhibits a smooth texture. [ABSTRACT FROM AUTHOR]

Digital image has been used in various fields as an essential carrier. Many color images have been constantly produced since their more realistic description, which takes up much storage space and network bandwidth. Thus, color image compression has become an essential key technology. Absolute Moment Block Truncation Coding (AMBTC) has been widely studied as one of the classical image compression methods. However, in the existing methods, the visual quality of the reconstructed images and the compression rate are all relatively low. Therefore, this paper proposes an enhanced AMBTC for color image compression using a color palette. In the proposed method, the K-means clustering algorithm is utilized for training the image's palette pattern. The color palette obtained by K-mean will be more suitable for reconstructing this image than the standard color palette, and the visual quality will be higher. The six clustered central pixels are matched with the palette through a color difference formula, and the obtained index values are used as the quantization levels. Huffman coding is used to build a bitmap to achieve a higher compression rate, that is, a lower bit rate. At last, a block of a color image can be represented by six index values and a bitmap. Experimental results and theoretical analysis demonstrate that the proposed method has better visual quality and bit rate than similar schemes. [ABSTRACT FROM AUTHOR]

In network security, safeguarding digital cyberspace infrastructure against malicious intrusions remains a paramount concern. This study addresses this imperative by proposing a novel approach that integrates digital watermarking, Huffman coding, and sophisticated attack classification techniques within the Controller Area Network (CAN) protocol framework. The proliferation of interconnected systems and the increasing sophistication of cyber threats necessitate novel strategies to fortify network defenses and ensure resilience in the face of adversarial activities. The research problem revolves around the need to develop an effective method for detecting and categorizing reconnaissance and violent attacks within cyberspace networks, particularly those utilizing the CAN protocol. Despite advancements in security protocols and intrusion detection systems, existing methodologies often fall short in accurately distinguishing between benign and malicious network activities, leaving critical infrastructure vulnerable to exploitation. This research uses digital watermarking to embed metadata within network packets, Huffman coding for efficient data compression, and advanced attack classification algorithms for real-time threat identification. The experimental results demonstrate the efficacy of the proposed approach in accurately differentiating between reconnaissance probes and violent attacks, thereby enabling timely and targeted responses to mitigate security threats. [ABSTRACT FROM AUTHOR]

BSuccinct is a collection of software focused on compact and succinct data structures that are both space and time efficient. It is written in Rust, a programming language well suited for scientific applications due to its emphasis on reliability and performance. BSuccinct provides libraries implementing minimal perfect hash functions, compressed static functions, bit vector manipulations, Huffman coding, and programs to benchmark them. It also includes libraries for several auxiliary operations, including binary serialization/deserialization and accurate float summation.

Reversible data hiding in encrypted images (RDHEI) can be used as an effective technique to protect the image content and manage the secret data embedded in the encrypted domain, it is useful for storage and management of the image outsourced to the cloud. In this paper, a hierarchical reversible data hiding with high payload in encrypted images is proposed. The pixels in original image are divided into the multiple linear regressions (MLR) predictable and the no MLR predictable. The MLR predictable pixels are processed to generate two levels label map of pixels by adaptive adjustment of predicted data and multiple bits prediction (MBP). The no MLR predictable pixels are used to generate label map of pixels (LMP) by using simple one bit prediction technique. The additional data produced by compression of three LMPs are embedded into the encrypted images. Thanks to the high correlation between pixels in the clear domain of image, the proposed scheme obtains a high embedding rate. The obtained average payloads are 2.9885 bpp, 3.759 bpp and 3.883 bpp for UCID, BOWS-2 and BOSS dataset, respectively. Extensive experiments including comparison tests, usability tests and ablation tests on some standard test images and three datasets are conducted to testify the effectiveness and usability of the proposed RDHEI, and the experimental results demonstrate that the proposed scheme outperforms the state-of-the-art RDHEI methods in payload on most images, the comparisons of performance show that great improvements have been made in the proposed method. [ABSTRACT FROM AUTHOR]

Biological data analyses involve researchers from several fields. To store and manipulate the huge volume of biological data obtained from different aspects is difficult. Compression algorithms considerably increase the storage medium's capacity while reducing the number of bits required representing the sequence. The core concept behind EIBDNASCA involves creating an optimized index file, which stores the non-repetitive bases (run length less than 8). This index file plays a crucial role in swiftly retrieving and reconstructing specific segments of the DNA sequence during the decompression process. In addition to the index file, EIBDNASCA incorporates a work file, which stores the repetitive bases (run length above 8) and represented in binary form. This work file allows the algorithm to perform various pre-processing and transformation tasks on the DNA sequence before generating the final compressed output. Finally, an enhanced Huffman coding technique is applied to the symbols present in the index file, optimizing the encoding process for more efficient compression. The proposed algorithm is examined using a variety of different GenBank database sources. Compression ratio, compression gain, and time required to compress and decompress the sequences are the metrics used to assess the performance. The experimental findings indicate that EIBDNASCA attains an average compression ratio of 1.23 bpb (bits per base) with an average compression gain of 84.52%. The average compression time is recorded at 0.590 seconds, and decompression is completed in 0.625 seconds. [ABSTRACT FROM AUTHOR]

In order to realize the real-time progressive compression of massive data and ensure the quality of compressed data, a real-time progressive compression method of massive data based on improved clustering algorithm is proposed in this paper. Through the micro clustering stage of birch method based on K-Medoids clustering, Clustering Feature Tree hierarchy is constructed and numerical clustering features are extracted; Taking this feature as the input of macro clustering order, the Clustering Feature Tree leaf nodes are clustered based on the improved K-Medoids clustering method, and the clustering data cluster set is output; The set is used as the original data of real-time progressive compression, and the data is denoised and compressed by lifting format wavelet transform. On this basis, Huffman coding is used to compress the data losslessly. The test results show that this method has good clustering effect under the optimal number of clustering centers, can complete the real-time progressive compression of a large number of data, and the availability of compressed data is more than 92%. [ABSTRACT FROM AUTHOR]

Copyright of International Journal of Engineering Research & Development (IJERAD) is the property of International Journal of Engineering Research & Development and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Today, telemedicine has become very essential, because it provides the possibility for the health centers, hospitals, and research centers to exchange medical and diagnostic data, via IoT. Since the volume of the generated medical information in IoT is very high, transmitting this data via channels of limited bandwidth is time-consuming; thus, the information should be compressed before transmission. Todays, some of the methods compress data significantly, but quality of the restored data in these methods is very low. Considering the importance of images and information for diagnostic and medical applications, desired quality is of great importance. Thus, this study tries to present a hybrid medical information compression technique such that quality of the restored information is desired and compression is efficient. This compression technique combines two lossless compression methods, including Huffman encoding and Lempel-Ziv-Welch (LZW) that rearranges information. In this combination, a binary information arrangement is used between Hoffman and LZW techniques so that the integration of binary information reaches an information mapping and, while simplifying the mapping, is completely different for each piece of information and makes this article stand out. The simulation results of the proposed method show that medical information including signal, text, and image is reduced by an average of 37.85%. Remarkably, quality of the restored image is not degraded. [ABSTRACT FROM AUTHOR]

The k-anonymity model is widely used as a data anonymization technique for privacy protection during the data release phase.However, with the advent of the big data era, the generation of vast amounts of data poses challenges to data storage.However, it is not feasible to expand the storage space infinitely by hardware upgrade, since the cost of memory is high and the storage space is limited.For this reason, data compression techniques can reduce storage costs and communication overhead.In order to reduce the storage space of the data generated by using anonymization techniques in the data publishing phase, a compression scheme was proposed for the original data and anonymized data of the k-anonymity model.For the original data of the k-anonymity model, the difference between the original data and the anonymized data was calculated according to the set rules and the pre-defined generalization level.Huffman coding compression was applied to the difference data according to frequency characteristics.By storing the difference data, the original data can be obtained indirectly, thus reducing the storage space of the original data.For anonymized data of the k-anonymity model, the anonymized data usually have high repeatability according to the generalization rules of the model or the pre-defined generalization hierarchy relations.The larger the value of k, the more generalized and repeatable the anonymized data becomes.The design of Huffman coding compression was implemented for anonymous data to reduce storage space.The experimental results show that the proposed scheme can significantly reduce the original data and the anonymous data compression rate of the k-anonymity model.Across five models and variousk-value settings,the proposed scheme reduces the compression rate of raw and anonymized data by 72.2% and 64.2% on average compared to the Windows 11 zip tool.

Abstract Background Presentation of visual stimuli can induce changes in EEG signals that are typically detectable by averaging together data from multiple trials for individual participant analysis as well as for groups or conditions analysis of multiple participants. This study proposes a new method based on the discrete wavelet transform with Huffman coding and machine learning for single-trial analysis of evenal (ERPs) and classification of different visual events in the visual object detection task. Methods EEG single trials are decomposed with discrete wavelet transform (DWT) up to the $$4{th}$$ 4 th level of decomposition using a biorthogonal B-spline wavelet. The coefficients of DWT in each trial are thresholded to discard sparse wavelet coefficients, while the quality of the signal is well maintained. The remaining optimum coefficients in each trial are encoded into bitstreams using Huffman coding, and the codewords are represented as a feature of the ERP signal. The performance of this method is tested with real visual ERPs of sixty-eight subjects. Results The proposed method significantly discards the spontaneous EEG activity, extracts the single-trial visual ERPs, represents the ERP waveform into a compact bitstream as a feature, and achieves promising results in classifying the visual objects with classification performance metrics: accuracies 93.60 $$\pm {6.5}$$ ± 6.5 , sensitivities 93.55 $$\pm {4.5}$$ ± 4.5 , specificities 94.85 $$\pm {4.2}$$ ± 4.2 , precisions 92.50 $$\pm {5.5}$$ ± 5.5 , and area under the curve (AUC) 0.93 $$\pm {0.3}$$ ± 0.3 using SVM and k-NN machine learning classifiers. Conclusion The proposed method suggests that the joint use of discrete wavelet transform (DWT) with Huffman coding has the potential to efficiently extract ERPs from background EEG for studying evoked responses in single-trial ERPs and classifying visual stimuli. The proposed approach has O(N) time complexity and could be implemented in real-time systems, such as the brain-computer interface (BCI), where fast detection of mental events is desired to smoothly operate a machine with minds.

Aiming at the problem that the existing spatial keyword group query problem did not consider the query requirements with exclusion keywords and time attributes, a time-aware group query problem with exclusion keywords (TEGSKQ) is proposed for the first time. To solve this problem effectively, this paper proposes a query method based on the EKTIR-Tree index and dominating group (EKTDG). This method first proposes the EKTIR-tree index, which incorporates Huffman coding and integrates Bloom filters to deal with excluded keywords in order to improve the hit rate of keyword queries, significantly improving the query efficiency and reducing the storage occupancy. Then, the Candidate algorithm is proposed based on the EKTIR-tree index to filter out the spatial–textual objects that meet the query's keywords and time requirements, narrowing the search space for subsequent queries on a large scale. To address the problem of the low efficiency of existing algorithms based on a spatial distance query, a distance-dominating group is defined and a pruning algorithm based on a spatial distance-dominating group is proposed, which is a refining process of query results and greatly improves the search efficiency of the query. Theoretical and experimental studies show that the proposed method can better handle group queries with exclusion keywords based on time awareness. [ABSTRACT FROM AUTHOR]

The rapid development of the Internet of Things has opened up commercial and economic opportunities, but there are also risks and threats to cybersecurity: a significant amount of private data is exposed in an untrusted cyberspace. In view of this issue, the research on reversible data hiding in encrypted image (RDHEI) aims to establish a secure and private communication channel between a sender and a recipient by embedding data into encrypted images. Therefore, this paper proposes a novel RDHEI scheme that employs a Huffman-based lossless image coding technique to achieve a high embedding capacity. Specifically, we compress the original image into a bitstream through the JPEG-like compression and then further encode the difference values between the reconstructed JPEG image and the original image via the Huffman coding. Due to the high compression rate and coding efficiency of the combined strategy, a large embedding space can be created. Our proposed scheme not only ensures a lossless recovery of the original image but also an error-free extraction of the secret messages. Several experiments are conducted to verify that our proposed scheme achieves a state-of-the-art performance compared with the prior art in terms of embedding capacity. [ABSTRACT FROM AUTHOR]

In wireless sensor network (WSN), the high volume of observe and transmitted data among sensor nodes make it requires to maintain the security. Even though numerous secure data transmission approaches designed over a network, an inadequate resource and the complex environment cause not able to used in WSNs. Moreover, secure data communication is a big challenging problem in WSNs especially for the military application. This paper proposes a dingo algorithm-based forwarder selection and huffman coding (DAHC) to improve authentication in internet of things (IoT) WSN. Initially, it detects the anomalous nodes by applying support vector machine (SVM) algorithm based on sensor node energy, node selfishness, and signal to noise ratio (SNR). Next, we using the dingo algorithm to select the forwarder node. This dingo algorithm computes the fitness function based on node degreee, node distance and node energy. Finally, the huffman coding to provide end to end authentication established on node energy from sender to receiver. During data transmission, the huffman coding to build the binary hop count value, it improves the authentication in the WSN. Performance results specify that this approach enhances the detection ratio and throughput. [ABSTRACT FROM AUTHOR]

The work aims to study the application of Deoxyribonucleic Acid (DNA) multi-source data storage in Digital Twins (DT). Through the investigation of the research status of DT and DNA computing, the work puts forward the concept of DNA multi-source data storage for DT. Raptor code is improved from the design direction of degree distribution function, and six degree function distribution schemes are proposed in turn in the process of describing the research method. Additionally, a quaternary dynamic Huffman coding method is applied in DNA data storage, combined with the improved concatenated code as the error correction code. Considering the content of cytosine deoxynucleotide (C) and guanine deoxynucleotide Guanine (G) and the distribution of homopolymer in DNA storage, the work proposes and verifies an improved concatenated code algorithm Deoxyribonucleic Acid-Improved Concatenated code (DNA-ICC). The results show that while the Signal-to-Noise Ratio (SNR) increases, the Bit Error Rate (BER) decreases gradually and the trend is similar. But the anti-interference ability of the degree distribution function optimized by the probability transfer method is better. The BER of DNA-ICC scheme decreases with the decrease of error probability, which is stronger than other error correction codes. Compared with the original concatenated code, it saves at least 1.65 s, and has a good control effect on homopolymer. When the size of homopolymer exceeds 4 nt, the probability of homopolymer is only 0.44%. The proposed Quaternary dynamic Huffman code and concatenated error correction code have excellent performance. [ABSTRACT FROM AUTHOR]

In recent years, the rise of biometric applications, particularly those centered around iris-based systems, has been significant. High data volumes inherent in these applications and the potential vulnerability of network links necessitate data compression in certain instances. The advantage of lossless compression methods is twofold: they maintain recognition performance without degradation while necessitating fewer computations for differentiation compared to their lossy counterparts. This study proposes a novel approach for lossless/lossy compression of iris biometric sample data across various public iris databases. Initially, the differences between successive images within each class are calculated, leveraging the strong correlation of images within each class. Subsequently, these differences are compressed using quadtree decomposition. This methodology was tested on six renowned iris databases: CASIA V1, CASIA V3, MMU1, MMU2, and UBIRIS Iris, all of which contain 8-bit grayscale images. The results indicate that the proposed strategy offers superior compression performance across different iris databases in comparison to existing methods. Notably, the results suggest that this method can be effectively integrated into an iris biometric recognition system, providing efficient iris image compression, especially when applied in its lossless form. [ABSTRACT FROM AUTHOR]

Modern, commonly used cryptosystems based on encryption keys require that the length of the stream of encrypted data is approximately the length of the key or longer. In practice, this approach unnecessarily complicates strong encryption of very short messages commonly used for example in ultra-low-power and resource-constrained wireless network sensor nodes based on microcontrollers (MCUs). In such cases, the data payload can be as short as a few bits of data while the typical length of the key is several hundred bits or more. The article proposes an idea of employing a complex of two algorithms, initially applied for data compression, acting as a standard-length encryption key algorithm to increase the transmission security of very short data sequences, even as short as one or a few bytes. In this article, we present and evaluate an approach that uses LZW and Huffman coding to achieve data transmission obfuscation and a basic level of security. [ABSTRACT FROM AUTHOR]

Lossless compression is still a challenging task in the case of microarray images. This research proposes two algorithms that aim to improve the lossless compression efficiency for high spatial resolution microarray images using general entropy codecs, namely Huffman and arithmetic coders and the image compression standard JPEG 2000. Using the standards ensures that decoders are available to reassess the images for future applications. Typically, microarray images have a bit-depth of 16. In proposed algorithm 1, every image's per bit-plane entropy profile is calculated to automatically determine a better threshold T to split the bit-planes into the foreground and background sub-images. T is initially set to 8. However, in algorithm 1, T is updated, balancing the average value of per bit-plane entropies of the segmented sub-images of an image for improved lossless compression results. Codecs are applied individually to the produced sub-images. Proposed algorithm 2 is designed to increase the lossless compression efficiency of any unmodified JPEG 2000-compliant encoder while reducing side information overhead. In this, pixel intensity reindexing and, thereby, changing the histograms of the same segmented sub-images obtained from algorithm 1 are implemented and confirmed to get better JPEG 2000 results in lossless mode than applying it to the original image. The lossless JPEG 2000 compression performance on microarray images is also compared to JPEG-LS in particular. The experiments are carried out to validate the methods on seven benchmark datasets, namely ApoA1, ISREC, Stanford, MicroZip, GEO, Arizona, and IBB. The average first-order entropy of the datasets above is calculated and compared for codecs and better than competitive efforts in the literature. [ABSTRACT FROM AUTHOR]

Copyright of Chinese Journal of Network & Information Security is the property of Beijing Xintong Media Co., Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract Malware attacks on the Android platform are rapidly increasing due to the high consumer adoption of Android smartphones. Advanced technologies have motivated cyber-criminals to actively create and disseminate a wide range of malware on Android smartphones. The researchers have conducted numerous studies on the detection of Android malware, but the majority of the works are based on the detection of generic Android malware. The detection based on malware categories will provide more insights about the malicious patterns of the malware. Therefore, this paper presents a detection solution for different Android malware categories, including adware, banking, SMS malware, and riskware. In this paper, a novel Huffman encoding-based feature vector generation technique is proposed. The experiments have proved that this novel approach significantly improves the efficiency of the detection model. This method makes use of system call frequencies as features to extract malware’s dynamic behavior patterns. The proposed model was evaluated using machine learning and deep learning methods. The results show that the proposed model with the Random Forest classifier outperforms some existing methodologies with a detection accuracy of 98.70%.

Convolutional neural networks have achieved remarkable success in computer vision research. However, to further improve their performance, network models have become increasingly complex and require more memory and computational resources. As a result, model compression has become an essential area of research in recent years. In this study, we focus on the best-case scenario for Huffman coding, which involves data with lower entropy. Building on this concept, we formulate a compression with a filter-wise difference minimization problem and propose a novel algorithm to solve it. Our approach involves filter-level pruning, followed by minimizing the difference between filters. Additionally, we perform filter permutation to further enhance compression. Our proposed algorithm achieves a compression rate of 94× on Lenet-5 and 50× on VGG16. The results demonstrate the effectiveness of our method in significantly reducing the size of deep neural networks while maintaining a high level of accuracy. We believe that our approach holds great promise in advancing the field of model compression and can benefit various applications that require efficient neural network models. Overall, this study provides important insights and contributions toward addressing the challenges of model compression in deep neural networks.

We propose a method of secure and reversible data hiding in encrypted images which ensures lossless recovery of image contents and data with very high embedding capacity. The proposed reversible data hiding method in encrypted images makes use of median edge detector predictor and Huffman coding for reducing the size of axillary information. The proposed method utilizes the spatial correlation of the image for reserving room for data embedding before encryption. Hiding capacity of each 2 × 2 block in the image is calculated based on the prediction error using the median edge detector. The hiding capacities of adjacent four blocks are labeled using Huffman coding, which reduces the size of the auxiliary information hidden along with secret data embedded in the encrypted image. Proposed method does data hiding without changing the characteristics of encrypted image for ensuring security and recovers data and image contents completely. The experimental results show that the proposed method hides higher payload with an average value of 3.34 bpp which is 25 percentage more than the state of the art methods. [ABSTRACT FROM AUTHOR]

Background: Presentation of visual stimuli can induce changes in EEG signals that are typically detectable by averaging together data from multiple trials for individual participant analysis as well as for groups or conditions analysis of multiple participants. This study proposes a new method based on the discrete wavelet transform with Huffman coding and machine learning for single-trial analysis of evenal (ERPs) and classification of different visual events in the visual object detection task. Methods: EEG single trials are decomposed with discrete wavelet transform (DWT) up to the 4 th level of decomposition using a biorthogonal B-spline wavelet. The coefficients of DWT in each trial are thresholded to discard sparse wavelet coefficients, while the quality of the signal is well maintained. The remaining optimum coefficients in each trial are encoded into bitstreams using Huffman coding, and the codewords are represented as a feature of the ERP signal. The performance of this method is tested with real visual ERPs of sixty-eight subjects. Results: The proposed method significantly discards the spontaneous EEG activity, extracts the single-trial visual ERPs, represents the ERP waveform into a compact bitstream as a feature, and achieves promising results in classifying the visual objects with classification performance metrics: accuracies 93.60 ± 6.5 , sensitivities 93.55 ± 4.5 , specificities 94.85 ± 4.2 , precisions 92.50 ± 5.5 , and area under the curve (AUC) 0.93 ± 0.3 using SVM and k-NN machine learning classifiers. Conclusion: The proposed method suggests that the joint use of discrete wavelet transform (DWT) with Huffman coding has the potential to efficiently extract ERPs from background EEG for studying evoked responses in single-trial ERPs and classifying visual stimuli. The proposed approach has O(N) time complexity and could be implemented in real-time systems, such as the brain-computer interface (BCI), where fast detection of mental events is desired to smoothly operate a machine with minds. [ABSTRACT FROM AUTHOR]

As an interesting technique that allows data extraction and image recovery without any loss, reversible data hiding in encrypted images is an area of great concern in the field of information security. In this paper, a new reversible data hiding method with high embedding capacity is proposed based on hybrid prediction and Huffman coding. The combination of two embedding mechanisms is innovatively designed to improve the embedding capacity according to different parts of the original image, i.e., the most significant bit-plane and the remaining seven bit-planes. In the first part of this method, the prediction value of each pixel is obtained by calculating the average value of its two neighboring pixels, and all of the most significant bits can be vacated to accommodate additional data. In the second part, the prediction value of each pixel is calculated using the median edge detector predictor, on which the tag map is built. Then, Huffman coding is used to compress the tag map so that a large amount of vacant space is obtained. Finally, the secret data can be embedded into the vacated space by directly using bit substitution. Compared with some recently reported methods, experimental results and analysis have demonstrated that an original image with high visual symmetry/quality can be recovered. Also, larger embedding capacity can be achieved, such as 3.3894 bpp and 3.2824 bpp, for BOSSBase and BOWS2 databases, respectively. [ABSTRACT FROM AUTHOR]

Nowadays, the massive use of multimedia data gives to data compression a fundamental role in reducing the storage requirements and communication bandwidth. Variable-length encoding (VLE) is a relevant data compression method that reduces input data size by assigning shorter codewords to mostly used symbols, and longer codewords to rarely utilized symbols. As it is a common strategy in many compression algorithms, such as the popular Huffman coding, speeding VLE up is essential to accelerate them. For this reason, during the last decade and a half, efficient VLE implementations have been presented in the area of General Purpose Graphics Processing Units (GPGPU). The main performance issues of the state-of-the-art GPU-based implementations of VLE are the following. First, the way in which the codeword look-up table is stored in shared memory is not optimized to reduce the bank conflicts. Second, input/output data are read/written through inefficient strided global memory accesses. Third, the way in which the thread-codes are built is not optimized to reduce the number of executed instructions. Our goal in this work is to significantly speed up the state-of-the-art implementations of VLE by solving their performance issues. To this end, we propose GVLE, a highly optimized implementation of VLE on GPU, which uses the following optimization strategies. First, the caching of the codeword look-up table is done in a way that minimizes the bank conflicts. Second, input data are read by using vectorized loads to exploit fully the available global memory bandwidth. Third, each thread encoding is performed efficiently in the register space with high instruction-level parallelism and lower number of executed instructions. Fourth, a novel inter-block scan method, which outperforms those of state-of-the-art solutions, is used to calculate the bit-positions of the thread-blocks encodings in the output bit-stream. Our proposed mechanism is based on a regular segmented scan performed efficiently on sequences of bit-lengths of 32 consecutive thread-blocks encodings by using global atomic additions. Fifth, output data are written efficiently by executing coalesced global memory stores. An exhaustive experimental evaluation shows that our solution is on average 2.6 × faster than the best state-of-the-art implementation. Additionally, it shows that the scan algorithm is on average 1.62 × faster if it utilizes our inter-block scan method instead of that of the best state-of-the-art VLE solution. Hence, our inter-block scan method offers promising possibilities to accelerate algorithms that require it, such as the scan itself or the stream compaction. [ABSTRACT FROM AUTHOR]

The emergence of technology and communication system paved way for the development of the internet of things (IoT). The IoT system generates diversified data and the quantity of the data is also huge. The IoT systems are developed to adhere to the situation and to make intelligent decisions in a specified time. Hence, the IoT system necessitates high processing and storage environment, which makes the effective response in a short‐duration. The data transmission across the mobile nodes and cloud service has made huge utilization of energy. The storage and energy consumption are considered as major issues in the IoT system whereas these issues will reflect in the performance of the IoT system. Initiation of edge computing into the IoT system permits the workload to be offloaded from the cloud providers, which is attained from the closer location of the source of data. This improves privacy, minimizes the saving time, and traffic. In this article, an Effective Energy usage and Data Compression Approach using Data Mining proposed for IoT data. The proposed approach is investigated by considering the driving behaviour and it achieves effective compression without influencing the quality of the data. The stress level of the driver is also identified with high accuracy. [ABSTRACT FROM AUTHOR]

Transformers have excelled in natural language processing and vision domains. This leads to the intriguing proposition: can Transformers be adapted to a more generalized framework, such as understanding general finite state machines? To explore this, we trained a Transformer network on compression algorithms such as Huffman and LZ4, viewing them as stepping stones towards mastering general finite state machines. Our analysis indicates that Transformers can adeptly internalize these methods and replicate essential states, echoing human-like interpretation via their attention mechanisms. This provides evidence of their capability to decipher practical finite state machines. Examining the attention maps offers insights into the Transformer’s methodology when engaging with these compression techniques. In Huffman encodings, the encoder predominantly focuses on input statistics to define the present state, which the decoder subsequently leverages to produce output bits. Remarkably, with a 2nd-order Markov input, the encoder’s attention is prominently directed at the previous two symbols, underscoring its proficiency in summarizing input statistics. The cross-attention maps further elucidate the exact association between input symbols and output bits. For the more complex LZ4 compression, the attention maps vividly portray the Transformer’s processing of input sequences and the close linkage between the input and resulting output bit stream. This study underscores the Transformer’s proficiency in comprehending compression algorithms and its keen ability to grasp input statistics, implying that its mechanisms, as illustrated by attention maps, provide profound interpretability.

In the compression of massive compound power quality disturbance (PQD) signals in active distribution networks, the compression ratio (CR) and reconstruction error (RE) act as a pair of contradictory indicators, and traditional compression algorithms have difficulties in simultaneously satisfying a high CR and low RE. To improve the CR and reduce the RE, a hybrid compression method that combines a strong tracking Kalman filter (STKF), sparse decomposition, Huffman coding, and run-length coding is proposed in this study. This study first uses a sparse decomposition algorithm based on a joint dictionary to separate the transient component (TC) and the steady-state component (SSC) in the PQD. The TC is then compressed by wavelet analysis and by Huffman and run-length coding algorithms. For the SSC, values that are greater than the threshold are reserved, and the compression is finally completed. In addition, the threshold of the wavelet depends on the fading factor of the STKF to obtain a high CR. Experimental results of real-life signals measured by fault recorders in a dynamic simulation laboratory show that the CR of the proposed method reaches as high as 50 and the RE is approximately 1.6%, which are better than those of competing methods. These results demonstrate the immunity of the proposed method to the interference of Gaussian noise and sampling frequency.

As recent machine translation models are mostly based on the attention-based neural machine translation (NMT), many well-known models such as Transformer or bidirectional encoder representations from Transformers (BERT) have been proposed. Along with algorithmic advancements, hardware acceleration methods for those attention-based neural machine translation models have also been introduced. However, the size of the parameters for attention-based NMT is also becoming larger to guarantee the satisfactory machine translation quality. Among various weights, linearization weights ( $W^{Q}$ , $W^{K}$ , $W^{V}$ , and $W^{O}$ ) account for a non-negligible portion (by up to 30%) among the entire parameters in the modern NMT models. In this paper, we propose a method for linearization weight compression and near-memory hardware decoder for fast and in-situ weight decompression. Our weight compression method exploits the fixed-point quantization along with Huffman coding which is selectively applied depending on the weight value distribution. Our hardware decoder decompresses the Huffman-coded weights near-memory to minimize the weight decoding latency. Our compression method shows 4.9–10.0 compression ratio with small NMT score drops across the five widely used attention-based NMT models (Transformer, Transformer-XL-base, Transformer-XL-large, BERT-base, and BERT-large). In addition, due to the reduced linearization weight size, our proposed method with near-memory decoding enables multi-head attention (MHA) execution latency reduction by 11.8%, on average, as compared to the baseline when considering the weight loading and initialization. In terms of the memory data transfer energy consumption, our proposed method leads to a memory energy saving of 16.1%, on average, as compared to the baseline.

Abstract To measure node importance, network scientists employ centrality scores that typically take a microscopic or macroscopic perspective, relying on node features or global network structure. However, traditional centrality measures such as degree centrality, betweenness centrality, or PageRank neglect the community structure found in real-world networks. To study node importance based on network flows from a mesoscopic perspective, we analytically derive a community-aware information-theoretic centrality score based on network flow and the coding principles behind the map equation: map equation centrality. Map equation centrality measures how much further we can compress the network’s modular description by not coding for random walker transitions to the respective node, using an adapted coding scheme and determining node importance from a network flow-based point of view. The information-theoretic centrality measure can be determined from a node’s local network context alone because changes to the coding scheme only affect other nodes in the same module. Map equation centrality is agnostic to the chosen network flow model and allows researchers to select the model that best reflects the dynamics of the process under study. Applied to synthetic networks, we highlight how our approach enables a more fine-grained differentiation between nodes than node-local or network-global measures. Predicting influential nodes for two different dynamical processes on real-world networks with traditional and other community-aware centrality measures, we find that activating nodes based on map equation centrality scores tends to create the largest cascades in a linear threshold model.

Malware attacks on the Android platform are rapidly increasing due to the high consumer adoption of Android smartphones. Advanced technologies have motivated cyber-criminals to actively create and disseminate a wide range of malware on Android smartphones. The researchers have conducted numerous studies on the detection of Android malware, but the majority of the works are based on the detection of generic Android malware. The detection based on malware categories will provide more insights about the malicious patterns of the malware. Therefore, this paper presents a detection solution for different Android malware categories, including adware, banking, SMS malware, and riskware. In this paper, a novel Huffman encoding-based feature vector generation technique is proposed. The experiments have proved that this novel approach significantly improves the efficiency of the detection model. This method makes use of system call frequencies as features to extract malware's dynamic behavior patterns. The proposed model was evaluated using machine learning and deep learning methods. The results show that the proposed model with the Random Forest classifier outperforms some existing methodologies with a detection accuracy of 98.70%. [ABSTRACT FROM AUTHOR]

As the rapid development of third-party storage and homomorphic encryption have profoundly stimulated the desire for secure communication, reversible data hiding in encrypted images has received widespread attention, since it allows lossless data conveying and perfect image recovery. In order to obtain secure reversible data hiding with high embedding capacity, a novel block embedding method is proposed, based on an adaptive recognition strategy for combined blocks in the binary image, with which the adjacent identical blocks can be integrated into a combination to reserve more spare bits for data accommodation. Furthermore, a fully reversible data hiding method for grayscale images in the encryption domain is designed. The secret data is hidden into lower bit-planes of the image while the original bits of those embedded lower pixels are recorded into the vacated space of higher bit-planes. The original image can be reconstructed flawlessly as well as the secret data being extracted without errors. To reinforce security, the original image and the secret data are encrypted and scrambled based on sequences generated with the high-dimension chaotic system. Due to its high sensitivity of initial values, the performance such as security and robustness is guaranteed. By comparing the PSNR value of the marked decrypted image and evaluating the quality of the extracted secret image, experimental results demonstrate that the proposed method can obtain higher embedding capacity, achieving 0.2700–0.3924 bpp increment over the state-of-the-art methods, and recover the marked decrypted image with high visual symmetry/quality, and efficiently resist against potential attacks, such as the histogram analysis, differential, brute-force, JPEG attacks, and so on. [ABSTRACT FROM AUTHOR]

The state-of-the-art performance for several real-world problems is currently reached by deep and, in particular, convolutional neural networks (CNN). Such learning models exploit recent results in the field of deep learning, leading to highly performing, yet very large neural networks with typically millions to billions of parameters. As a result, such models are often redundant and excessively oversized, with a detrimental effect on the environment in terms of unnecessary energy consumption and a limitation to their deployment on low-resource devices. The necessity for compression techniques able to reduce the number of model parameters and their resource demand is thereby increasingly felt by the research community. In this paper we propose the first extensive comparison, to the best of our knowledge, of the main lossy and structure-preserving approaches to compress pre-trained CNNs, applicable in principle to any existing model. Our study is intended to provide a first and preliminary guidance to choose the most suitable compression technique when there is the need to reduce the occupancy of pre-trained models. Both convolutional and fully-connected layers are included in the analysis. Our experiments involved two pre-trained state-of-the-art CNNs (proposed to solve classification or regression problems) and five benchmarks, and gave rise to important insights about the applicability and performance of such techniques w.r.t. the type of layer to be compressed and the category of problem tackled. [ABSTRACT FROM AUTHOR]

Building a balanced relation between image quality and the payload, the robustness of the method in facing electronic attacks and securing data, all the mentioned processes represent the main challenge in steganography. Here, a novel approach to steganography is suggested using Vigenere Cipher and Huffman Coding methods to encrypt and compress the mystery message content. This approach will raise the security and ensure the message content cannot be extracted without earlier knowledge of decrypting rules and the Huffman Dictionary Table. Later, the image is segmented into blocks, size (w*h) groups for each block and with each group having n pixels. Subsequently, the knight tour algorithm and arbitrary function are utilized to select which blocks and groups can be used to conceal the mystery digit within a specific pixel in the group randomly. This is to address the weakness of the Exploiting Modification Direction (EMD) technique that uses a serial selection to enhance the robustness of the suggested scheme. The EMD technique is then utilized to insert the mystery digits inside a specific pixel. Later, the chi-square method is employed to apply statistical attacks on the stego-image to estimate the suggested scheme robustness. The empirical outcomes show that the suggested scheme is more efficient compared to the old Steganography schemes with respect to Imperceptibility by PSNR of 55.71 dB, the Payload of 52,400 bytes and the robustness.

In the medical industry, the amount of data that can be collected and kept is currently increasing. As a result, in order to handle these large amounts of data efficiently, compression methods must be re-examined while taking the algorithm complexity into account. An image processing strategy should be explored to eliminate the duplication image contents, so boosting the capability to retain or transport data in the best possible manner. Image Compression (IC) is a method of compressing images as they are being stored and processed. The information is preserved in a lossless image compression technique which allows for exact image reconstruction from compressed data with retain the quality of image to higher possible extend but it does not significantly decrease the size of the image. In this research work, the encoding algorithm is applied to various medical images such as brain image, dental x-ray image, hand x ray images, breast mammogram images and skin image can be used to minimize the bit size of the image pixels based on the different encoding algorithm such as Huffman, Lempel-Ziv-Welch (LZW) and Run Length Encoding (RLE) for effective compression and decompression without any quality loss to reconstruct the image. The image processing toolbox is used to apply the compression algorithms in MATLAB. To assess the compression efficiency of various medical images using different encoding techniques and performance indicators such as Compression Ratio (CR) and Compression Factor (CF). The LZW technique compresses binary images; however, it fails to generate a lossless image in this implementation. Huffman and RLE algorithms have a lower CR value, which means they compress data more efficiently than LZW, although RLE has a larger CF value than LZW and Huffman. When fewer CR and more CF are recorded, RLE coding becomes more viable. Finally, using state-of-the-art methodologies for the sample medical images, performance measures such as PSNR and MSE is retrieved and assessed.

D-2-D (Device to Device) communication is essential in 5th Generation communication networks for allowing communication between devices without transmitting data through the network infrastructure and it plays a major challenge in assuring the effectiveness and security of D-2-D (Device to Device) communication. In this research work, a hybrid method is proposed for the security and effectiveness of Device-to-Device communication in 5G networks by combining AES encryption with Huffman coding techniques and achieving enhanced security for D-2-D (Device to Device) communication while maintaining efficient data transmission. The AES method is known for protecting Device-to-Device (D2D) communication in 5th Generation networks. An effective security solution is provided by the AES algorithm for D-2-D (Device to Device) communication in 5th Generation networks, guaranteeing the secrecy of data exchanged between devices. This work enhances the privacy of D-2-D (Device to Device) communication, making it suitable for 5th Generation networks and it provides robust encryption of data while Huffman coding reduces the data size for efficient transmission using Python programming language. [ABSTRACT FROM AUTHOR]

With the effect of digitalization, the transfer of all text documents over the Internet rather than human transmission has increased, and this situation has revealed the idea that text documents can be used as a carrier that can safely store information. Realizing that methods such as word-line shifting, usage of spaces, replacement of the word with its synonym are fragile against steganalysis, led to new searches and it was determined that deep learning models were more resistant to detecting the presence of hidden words. In this study, the text generation based on the information that is wanted to be hidden without a carrier text, both at word and character level, was performed. Arithmetic coding, perfect tree and Huffman coding methods were used as secret information embedding methods in text generation based on word level. In this part of the study, bidirectional LSTM architecture with attention mechanism was created as language model. In text generation based on character level, a new secret information embedding algorithm is created by combining the LZW compression algorithm with the Char Index (LZW-Char Index Encoding) method. The character-level model is created as a result of using the encoder–decoder architecture together with bidirectional LSTM and Bahdanau attention. The proposed method was evaluated from the perspectives of information embedding efficiency, information imperceptibility and hidden information capacity. As a result of the experiments, it was determined that the method exceeded the state-of-the-art performance and was more resistant to steganalysis. [ABSTRACT FROM AUTHOR]

Recently, reversible data hiding in encrypted images (RDHEI) based on pixel prediction has been a hot topic. However, existing schemes still employ a pixel predictor that ignores pixel changes in the diagonal direction during prediction, and the pixel labeling scheme is inflexible. To solve these problems, this paper proposes reversible data hiding in encrypted images based on adaptive prediction and labeling. First, we design an adaptive gradient prediction (AGP), which uses eight adjacent pixels and combines four scanning methods (i.e., horizontal, vertical, diagonal, and diagonal) for prediction. AGP can adaptively adjust the weight of the linear prediction model according to the weight of the edge attribute of the pixel, which improves the prediction ability of the predictor for complex images. At the same time, we adopt an adaptive huffman coding labeling scheme, which can adaptively generate huffman codes for labeling according to different images, effectively improving the scheme’s embedding performance on the dataset. The experimental results show that the algorithm has a higher embedding rate. The embedding rate on the test image Jetplane is 4.2102 bpp, and the average embedding rate on the image dataset Bossbase is 3.8625 bpp. [ABSTRACT FROM AUTHOR]