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The primary aim of the article is to compare the usefulness of paper and electronic versions of OALDCE7 (Wehmeier 2005) for language encoding, decoding and learning. It is explained why, in contrast to Dziemianko's (2010) findings concerning COBUILD6 (Sinclair 2008), but in keeping with her observations (Dziemianko 2011) with regard to LDOCE5 (Mayor 2009), the e-version of OALDCE7 proved to be no better for language reception, production and learning than the dictionary in book form. An attempt is made to pinpoint the micro- and macrostructural design features which make e-COBUILD6 a better learning tool than e-OALDCE7 and e-LDOCE5. Recommendations concerning further research into the significance of the medium (paper vs. electronic) in the process of dictionary use conclude the study. The secondary aim which the paper attempts to achieve is to present the status of replication as a scientific research method and justify its use in lexicography.

The Latent Space Perspicacity and Interpretation Enhancement (LS-PIE) framework enhances dimensionality reduction methods for linear latent variable models (LVMs). This paper extends LS-PIE by introducing an optimal latent discovery strategy to automate identifying optimal latent dimensions and projections based on user-defined metrics. The latent condensing (LCON) method clusters and condenses an extensive latent space into a compact form. A new approach, latent expansion (LEXP), incrementally increases latent dimensions using a linear LVM to find an optimal compact space. This study compares these methods across multiple datasets, including a simple toy problem, mixed signals, ECG data, and simulated vibrational data. LEXP can accelerate the discovery of optimal latent spaces and may yield different compact spaces from LCON, depending on the LVM. This paper highlights the LS-PIE algorithm’s applications and compares LCON and LEXP in organising, ranking, and scoring latent components akin to principal component analysis or singular value decomposition. This paper shows clear improvements in the interpretability of the resulting latent representations allowing for clearer and more focused analysis.

Abstract Existing traffic signal control systems present many limitations including fixed signal timing schemes, insufficient efficiency and flexibility, and difficulty in adapting to the changing traffic flows. In recent years, the development of deep reinforcement learning (RL) has shown great research potential and application prospects. This paper proposes an intersection signal control method based on the proximal policy optimization (PPO) method. Specifically, this paper uses the value vector representation method of traffic characteristics to encode the traffic state and then feeds the state encoding into the long short‐term memory (LSTM) network to obtain the signal phase output. Finally, to obtain the optimal signal control strategy, the PPO algorithm is utilized to train the neural network and adjust the signal phase. The proposed algorithm is benchmarked against other classic RL and adaptive signal control schemes in an experimental environment based on the traffic simulation software simulation of urban mobility (SUMO). Experimental results showed that the proposed algorithm greatly improves traffic efficiency. Specifically, the mean velocity of the vehicles increases by 45.09%, and the mean occupancy rate of each lane, the length of the longest jams during each step, and the mean halting duration dropped by 21.38%, 25.86%, and 12.94%, respectively.

Rumoh aceh is a Acehnese traditional house that is one of the products and artifacts of the Acehnese culture. Efforts to preserve rumoh aceh often focus on physical aspects or aspects of form that are seen directly. It is has led to the discovery of many new buildings that now adopt the architectural form of rumoh aceh without a more comprehensive elaboration and transformation process. A concerted effort is required to present rumoh aceh architecture in a modern context. Transformation is one of the methods in architectural design to be able to present new ideas that are creative and innovative. The transformation in this paper is obtained through an encoding process that employs a semiotic approach as a method for bridging the rumoh aceh architecture transformation process. The encoding process is carried out by reinterpreting the values of rumoh aceh architecture obtained from previous research. This paper aims to gain knowledge regarding the architectural design process that can present a creation that represents regional culture, in this case, to present rumoh aceh architecture in a modern context

Brain-inspired architectures are gaining increased attention, especially for edge devices to perform cognitive tasks utilizing its limited energy budget and computing resources. Hyperdimensional computing (HDC) paradigm is an emerging framework inspired by an abstract representation of neuronal circuits’ attributes in the human brain. That includes a fully holographic random representation, high-dimension vectors representing data, and robustness to uncertainty. The basic HDC pipeline consists of an encoding, training and comparison stages. The encoding algorithm maps different representations of inputs into a single class and stores them in the associative memory (AM) throughout the training stage. Later, during the inference stage, the similarity is computed between the query vector, which is encoded using the same encoding model, and the stored classes in the AM. HDC has shown promising results for 1D applications using less power, and lower latency than state-of-the-art digital neural networks (DNN). While in 2D applications, convolutional neural network (CNN) still achieves higher classification accuracy at the expense of more computations. In this paper, a comprehensive study on the HDC paradigm, main algorithms, and its implementation is presented. Moreover, the main state-of-the-art HDC architectures for 1D and 2D applications are highlighted. The article also analyzes two competing paradigms, namely, HDC and CNN, in terms of accuracy, complexity, and the number of operations. The paper concluded by highlighting challenges and recommendations for future directions on the HDC framework.

Nowadays, cybersecurity has grown into a more significant and difficult sci-entific issue. The recognition of threats and attacks meant for knowledge and safety on the internet is growing harder to detect. Since cybersecurity guar-antees the privacy and security of data sent via the Internet, it is essential, while also providing protection against malicious attacks. Encrypt has grown into an answer that has become an essential element of information security systems. To ensure the security of shared data, including text, images, or videos, it is essential to employ various methods and strategies. This study delves into the prevalent cryptographic methods and algorithms utilized for prevention and stream encryption, examining their encoding techniques such as advanced encryption standard (AES), Blowfish, Twofish, Salsa20, and ChaCha20. The primary objective of this research is to identify the optimal times and throughputs (speeds) for data encryption and decryption processes. The methodology of this study involved selecting five distinct types of images to compare the outcomes of the techniques evaluated in this research. The as-sessment focused on processing time and speed parameters, examining visual encoding and decoding using Java as the primary platform. A comparative analysis of several symmetric key ciphers was performed, focusing on handling large datasets. Despite this limitation, comparing different images helped evaluate the techniques' novelty. The results showed that ChaCha20 had the best average time for both encryption and decryption, being over 50% faster than some other algorithms. However, the Twofish algorithm had lower throughput during testing. The paper concludes with findings and suggestions for future improvements.

DNA steganography is a technique for securely transmitting important data using DNA sequences. It involves encrypting and hiding messages within DNA sequences to prevent unauthorized access and decoding of sensitive information. Biometric systems, such as fingerprinting and iris scanning, are used for individual recognition. Since biometric information cannot be changed if compromised, it is essential to ensure its security. This research aims to develop a secure technique that combines steganography and cryptography to protect fingerprint images during communication while maintaining confidentiality. The technique converts fingerprint images into binary data, encrypts them, and embeds them into the DNA sequence. It utilizes the Feistel network encryption process, along with a mathematical function and an insertion technique for hiding the data. The proposed method offers a low probability of being cracked, a high number of hiding positions, and efficient execution times. Four randomly chosen keys are used for hiding and decoding, providing a large key space and enhanced key sensitivity. The technique undergoes evaluation using the NIST statistical test suite and is compared with other research papers. It demonstrates resilience against various attacks, including known-plaintext and chosen-plaintext attacks. To enhance security, random ambiguous bits are introduced at random locations in the fingerprint image, increasing noise. However, it is important to note that this technique is limited to hiding small images within DNA sequences and cannot handle video, audio, or large images.

Abstract Face regions containing rich semantic information appear frequently in the videos. As the video resolution increase dramatically, the face regions will inevitably attract more attentions. This paper proposes a face perception based coding scheme to improve the visual quality of the face regions in UHD videos. A specially tailored face perception model is first utilized to precisely and quickly locate the face regions. Then, a face perception map is generated based on a hierarchical mapping algorithm. Finally, the face perception map is employed as a guidance to optimize the encoding process, including mode decision, block partition and bit allocation. The proposed method is implemented on HEVC to demonstrate the effectiveness. Experimental results on a set of 4K test sequences show that the proposed method can obviously improve the objective and subjective quality of the face regions, while causing only slight quality decline over the rest of the frame. Additionally, the computation required for mode decision and block partition is reduced, thereby saving encoding time cost.

In view of the shortcomings of traditional fault diagnosis methods based on time domain vibration analysis, which require complicated calculations of feature vectors, and are over-dependent on a prior diagnosis knowledge, effort for the design of the feature extraction algorithms, and have limited ability to extract the complex relationships in fault signals, in this paper we propose a convolutional neural network (CNN) framework for machine health monitoring based on the encoding of one-dimension (1-D) time series to two-dimension (2-D) images. This paper defines a new Gram matrix and through the Python programming environment, we emulate the new Gram matrix in 2-D images, thus feature extraction and recognition can be performed by CNNs. The proposed method which is tested on two datasets, including multi-stage centrifugal fan dataset for our lab, motor bearing dataset for Case Western Reserve University, has achieved prediction average accuracy of 94.07% and 96.28%, respectively. The results have been compared with other deep learning and traditional methods, including Recurrent neural network (RNN), Support Vector Machines (SVM), Multi-Genetic algorithm, shallow CNN and BP neural network. The results show that the method can improve fault diagnosis accuracy in an effective way and stability than other advanced techniques.

In the last decade, various mobile applications have been developed to improve and measure spatial abilities using different spatial tests and tasks through augmented reality (AR), Virtual Reality (VR), or embedded 3D viewers. The Mental Cutting Test (MCT) is one of the most well-known and popular tests for this purpose, but it needs a vast number of tasks (scenarios) for effective practice and measurement. We have recently developed a script-aided method that automatically generates and permutes Mental Cutting Test scenarios and exports them to an appropriate file format (to GLB (glTF 2.0) assets) representing the scenarios. However, the significant number of permutations results in more than 1,000,000 assets, requiring more than 6 GB of storage space. This paper introduces an encoding scheme consisting of four stages to handle this issue through significantly reducing the storage space, making the app suitable for everyday individual use, even on a mobile phone. The proposed method encodes a subset of assets from which it can decode the whole dataset with 3% time complexity compared to classical Blender’s computations, exceeding the compression ratio of 10,000 and storage space saving 99.99%. This paper explains the features of the original assets, introduces the encoding and decoding functions with the format of documents, and then measures the solution’s efficiency based on our dataset of MCT scenarios.

Distributed Denial of Service (DDoS) attacks pose a significant threat to the security of networking systems, as they can cause widespread disruption and even bring down entire distributed systems platforms. In this paper, we propose an approach called the DEQSVC that leverages quantum machine learning techniques to detect DDoS attacks with high accuracy. The DEQSVC integrates the most efficient dimensionality reduction techniques, a robust feature map method, and an efficient kernel estimation technique to improve data encoding, learning process, and detection accuracy. To evaluate the performance of the proposed DEQSVC, we conducted simulations using the Qiskit platform and executed the approach on an IBM quantum computer. Our results demonstrate that the DEQSVC outperforms several benchmark algorithms commonly used in intrusion detection systems. Specifically, the DEQSVC achieves a detection accuracy of 99.49, indicating its effectiveness as a highly accurate and efficient method for detecting DDoS attacks.

Background: In recent times, transmission of information over a wireless channel is increasing at an exponential rate. Applications based on IOT will exceed all form of data available over the internet, it can be in the form of data based on videos and images which alone size up to 70% of the global traffic. Methodology: In this paper, an encoding technique based on discrete cosine transform (DCT) have been utilized to reduce information of a CT image that is unwanted or a human eye is unable to perceive. For medical images, preserving maximum information in order to properly diagnose any disease is important and for such application, the amount of quantization needed to gain an image with minimum error is to observe the Correlation index (CI). Results: CI at 50% Quality Quantization Table (QQT) for image 1 was found to be 0.9981 and its size reduced to 0.403 MB, it means that the compressed image is 99.8% similar to the original image. But its similarity reduces with the increase in QQT, its size will also decrease by at this point quality will degrade too. Conclusion: In order to store images on the drive, it is imperative to apply a compression technique. In this paper, a compression technique is proposed utilizing a DCT algorithm to reduce an image size with different QQT provided for quality assessment. Each image has a different set of information and to cater their differences this QQT can be manipulated for best possible quality with acceptable size on disk.

The administrative yearbooks of the Seine departments and of the city of Paris are crucial sources for understanding the organization of these institutions, and also for finding one’s way around the archives related to the administration of the city. The project carried out as part of the Archival City programme aimed to facilitate access to these yearbooks. Digitalized by the Archives de Paris, which hold them, these documents have been transcribed and encoded in XML-TEI format. Using this fundamental basis, data exports have been performed in handy formats and a visualisation website is currently under construction (planned for the beginning of 2024). Our paper intends to present this project, the sources we have been working on and the results we have obtained. It especially aims to enable the scientific community to use, reuse and enhance the resultant datasets.

The present paper describes a project aiming at editing University glosses of the thirteenth century copied around the Aristotelian treatises on nature. No reference edition of this corpus has been carried out. Furthermore, editions of glosses in paper or electronic form do not always meet the reader’s needs, among which the rendition of complex manuscript traditions and of the link between the glosses and the text, or a simplified interface for reading the text and the marginal and interlinear glosses sequentially. XML-TEI encoding allows to render these features and to produce outputs to paper and electronic formats, thanks to XSLT transformation sheets. The electronic format enables the editions of ancient sources to be read in new ways. Through the creation of encoding and transforming tools and methodologies, the project seeks the constitution of a resource center for scientific edition.

Most of the quantum error correction methods are symmetric. Symmetric methods are implemented by considering the amplitude of bit flip(X) and phase flip(Z) errors as same. With the quantum experiments, it is observed that the amplitude of Z errors are more compared to X errors. Due to which the need of asymmetric error correction has increased. This paved a path for the development of asymmetric error correction methods. In this paper, we discussed the concept of asymmetric quantum error correction (AQEC) and proposed an efficient approach for AQEC with encoding, syndrome measurement and decoding operations with increased fidelity to 85.89% and reduced circuit depth to 48%.

Abstract The Consultative Committee for Space Data Systems, followed by all national and international space agencies, has updated the Telecommand Coding and Synchronization sublayer to introduce new powerful low-density parity-check (LDPC) codes. Their large coding gains significantly improve the system performance and allow new Telecommand services and profiles with higher bit rates and volumes. In this paper, we focus on the Telecommand transmitter implementation in the Ground Station baseband segment. First, we discuss the most important blocks and we focus on the most critical one, i.e., the LDPC encoder. We present and analyze two techniques, one based on a Shift Register Adder Accumulator and the other on Winograd convolution both exploiting the block circulant nature of the LDPC matrix. We show that these techniques provide a significant complexity reduction with respect to the usual encoder mapping, thus allowing to obtain high uplink bit rates. We then discuss the choice of a proper hardware or software platform, and we show that a Central Processing Unit-based software solution is able to achieve the high bit rates requested by the new Telecommand applications. Finally, we present the results of a set of tests on the real-time software implementation of the new system, comparing the performance achievable with the different encoding options.

Resource description framework (RDF) is a metadata model and information description specification recommended by W3C,which is widely used in various fields.To track changes in RDF data over time,temporal information is introduced into the RDF framework.With the rapid growth of temporal RDF data,effective management of temporal RDF data is necessary.A reasonable index mechanism can achieve efficient storage and query of data.In this paper,we first present a temporal RDF data mo-del.We propose a specific one-dimensional coding scheme,which represent temporal data simply and extend the existing RDF data model with lower overhead.Furthermore,we present its two levels of indexes based on neighborhood structure.The first one uses dynamic counting filter to index the neighborhood information of the node,and the second builds the B+ tree to index the temporal RDF data related to each node.Moreover,large-scale temporal RDF data can be updated.Experimental results show that the proposed method is around 35% better than the comparison method in most cases,and it is scalable and effective.

Pandemi Covid-19 telah memaksa pemerintah dan masyarakat untuk melakukan pembelajaran daring. Meskipun pandemi telah mulai mereda, bukan berarti bebas dari ancaman. Selain untuk antisipasi pandemi, pembelajaran daring bisa tetap dilakukan untuk melengkapi pembelajaran luring. Teknologi Metaverse dianggap merupakan salah satu terobosan penting untuk menghadirkan pembelajaran daring yang lebih berkualitas. Ada empat jenis simulasi dalam teknologi Metaverse, yaitu Augmented Reality, Lifelogging, Mirror World, dan Virtual Reality. Paper ini merupakan studi literature yang bertujuan untuk membahas apakah pola dan metode pembelajaran yang mungkin dilakukan dengan menggunakan teknologi Metaverse tersebut cukup efektif untuk proses pembentukan memori pada siswa. Dari hasil analisis, jika diterapkan dalam proses pembelajaran, Teknologi Metaverse diprediksi akan mampu meningkatkan asosiasi, elaborasi dan imajinasi dalam proses encoding, namun sangat mungkin menyebabkan gangguan atensi, menimbulkan masalah dalam proses pembentukan identitas diri dan bisa memunculkan berbagai macam problematika sosial dan adiksi game online.

Healthcare systems capture patients' data using different medical equipment and store it in the databases with a continual increase in data volume. The continuous processing and sharing of this massive data are rising concerns in live data transferring over the networks. Sending patient data to the distant remote user without proper compressing format requires high latency in the communication channels. Any alternation of data transmitted via the communication medium may also cause issues in assuring data authentication and integrity. For solving the problems, watermarking method is being applied to ensure such security, which has a cheaper computational cost. Various watermarking mechanisms are available for ensuring health data security, especially for medical images. Watermarking on the text was not used yet due to the lack of efficient technique. This paper proposes a secured compression technique for patient live-text data while sharing them remotely over a bandwidth-deficient channel. To test the proposed system, we use patient data. The result indicates that the proposed strategy outperforms the existing compression methods and is robust enough to provide data integrity and authentication.

Dynamic Adaptive Streaming over HTTP (DASH) has prevailed as the dominant way of video transmission over the Internet. This technology is based on receiving small sequential video segments from a server. However, one challenge that has not been adequately examined is the obtainment of video segments in a way that serves both the needs of the network and the improvement in the Quality of Experience (QoE) of the users. One effective way to achieve this is to implement and study caching and DASH technologies together. This paper investigates this issue by simulating a network with multiple video servers and a video client. It then implements both the peer-to-many communications in the context of adaptive video streaming and the video server caching algorithm based on proposed criteria that improve the status of the network and/or the user. Specifically, we investigate the scenario of delivering DASH-based content with the help of an intermediate server, apart from a main server, to demonstrate possible caching benefits for different sizes of intermediate storage servers. Extensive experimentation using emulation reveals the interplay and delicate balance between caching and DASH, guiding such network design decisions. A general tendency found is that, as the available buffer size increases, the video playback quality increases to some extent. However, at the same time, this improvement is linked to the random cache selection algorithm.

Abstract The Lateral Geniculate Nucleus (LGN) represents one of the major processing sites along the visual pathway. Despite its crucial role in processing visual information and its utility as one target for recently developed visual prostheses, it is much less studied compared to the retina and the visual cortex. In this paper, we introduce a deep learning encoder to predict LGN neuronal firing in response to different visual stimulation patterns. The encoder comprises a deep Convolutional Neural Network (CNN) that incorporates visual stimulus spatiotemporal representation in addition to LGN neuronal firing history to predict the response of LGN neurons. Extracellular activity was recorded in vivo using multi-electrode arrays from single units in the LGN in 12 anesthetized rats with a total neuronal population of 150 units. Neural activity was recorded in response to single-pixel, checkerboard and geometrical shapes visual stimulation patterns. Extracted firing rates and the corresponding stimulation patterns were used to train the model. The performance of the model was assessed using different testing data sets and different firing rate windows. An overall mean correlation coefficient between the actual and the predicted firing rates of 0.57 and 0.7 was achieved for the 10 ms and the 50 ms firing rate windows, respectively. Results demonstrate that the model is robust to variability in the spatiotemporal properties of the recorded neurons outperforming other examined models including the state-of-the-art Generalized Linear Model (GLM). The results indicate the potential of deep convolutional neural networks as viable models of LGN firing.

Record linkage is the process of identifying records that corresponds to the same real-world entities across different databases. Due to the absence of unique entity identifiers, record linkage is often based on quasi-identifying values of entities (individuals) such as their names and addresses. However, regulatory ethical and legal obligations can limit the use of such personal information in the linkage process in order to protect the privacy and confidentiality of entities. Privacy-preserving record linkage (PPRL) aims to develop techniques that enable the linkage of records without revealing any sensitive or confidential information about the entities that are represented by these records. Over the past two decades various PPRL techniques have been proposed to securely link records between different databases by encrypting and/or encoding sensitive values. However, some PPRL techniques, such as popular Bloom filter encoding, have shown to be susceptible to privacy attacks. These attacks exploit the weaknesses of PPRL techniques by trying to reidentify encrypted and/or encoded sensitive values. In this paper we propose a taxonomy for analysing such attacks on PPRL where we categorise attacks across twelve dimensions, including different types of adversaries, different attack types, assumed knowledge of the adversary, the vulnerabilities of encoded and/or encrypted values exploited by an attack, and assessing the success of attacks. Our taxonomy can be used by data custodians to analyse the privacy risks associated with different PPRL techniques in terms of existing as well as potential future attacks on PPRL.

Record linkage is the process of identifying records that corresponds to the same real-world entities across different databases. Due to the absence of unique entity identifiers, record linkage is often based on quasi-identifying values of entities (individuals) such as their names and addresses. However, regulatory ethical and legal obligations can limit the use of such personal information in the linkage process in order to protect the privacy and confidentiality of entities. Privacy-preserving record linkage (PPRL) aims to develop techniques that enable the linkage of records without revealing any sensitive or confidential information about the entities that are represented by these records. Over the past two decades various PPRL techniques have been proposed to securely link records between different databases by encrypting and/or encoding sensitive values. However, some PPRL techniques, such as popular Bloom filter encoding, have shown to be susceptible to privacy attacks. These attacks exploit the weaknesses of PPRL techniques by trying to reidentify encrypted and/or encoded sensitive values. In this paper we propose a taxonomy for analysing such attacks on PPRL where we categorise attacks across twelve dimensions, including different types of adversaries, different attack types, assumed knowledge of the adversary, the vulnerabilities of encoded and/or encrypted values exploited by an attack, and assessing the success of attacks. Our taxonomy can be used by data custodians to analyse the privacy risks associated with different PPRL techniques in terms of existing as well as potential future attacks on PPRL.

With the development of various information and communication technologies, the amount of big data has increased, and distributed file systems have emerged to store them stably. The replication technique divides the original data into blocks and writes them on multiple servers for redundancy and fault tolerance. However, there is a symmetrical space efficiency problem that arises from the need to store blocks larger than the original data. When storing data, the Erasure Coding (EC) technique generates parity blocks through encoding calculations and writes them separately on each server for fault tolerance and data recovery purposes. Even if a specific server fails, original data can still be recovered through decoding calculations using the parity blocks stored on the remaining servers. However, matrices generated during encoding and decoding are redundantly generated during data writing and recovery, which leads to unnecessary overhead in distributed file systems. This paper proposes a cache-based matrix technique that uploads the matrices generated during encoding and decoding to cache memory and reuses them, rather than generating new matrices each time encoding or decoding occurs. The design of the cache memory applies the Weighting Size and Cost Replacement Policy (WSCRP) algorithm to efficiently upload and reuse matrices to cache memory using parameters known as weights and costs. Furthermore, the cache memory table can be managed efficiently because the weight–cost model sorts and updates matrices using specific parameters, which reduces replacement cost. The experiment utilized the Hadoop Distributed File System (HDFS) as the distributed file system, and the EC volume was composed of Reed–Solomon code with parameters (6, 3). As a result of the experiment, it was possible to reduce the write, read, and recovery times associated with encoding and decoding. In particular, for up to three node failures, systems using WSCRP were able to reduce recovery time by about 30 s compared to regular HDFS systems.

In this paper, an efficient lossy compression approach using adaptive-block polynomial curve-fitting encoding is proposed. The main idea of polynomial curve fitting is to reduce the number of data elements in an image block to a few coefficients. The proposed approach consists of two processes: encoding and decoding. In the encoding process, the coefficient matrix is created by representing each block of the image with a first- or second-order two-dimensional polynomial. The encoded block size of the image is variable. The polynomial order and the encoded block size are determined dynamically depending on the value of a threshold. A prefix code of two bits is used to differentiate the encoding states. Uniform quantization is applied to the coefficient matrix to store these coefficients effectively. In the decoding process, the reconstructed (decompressed) image is built from the quantized coefficient matrix. The fitting variables are two-dimensional (x, y). The encoding and decoding processes require a single image scan without the need to transfer the matrix to another domain. Experimentally, a high compression ratio is achieved at an acceptable quality for both gray and color images. The results are comparable to those of most recent studies.

In this paper, we present linear systematic error-correcting codes $\mathcal{L}_{k}$ and $\mathcal{L}^{+}_{k}$ which are the results of our research on the sub-exceeding functions. Given an integer $ k $ such that $ k \geq 3$, these two codes are respectively $[2k,k]$ and $[3k,k]$ linear codes. The minimum distance of $\mathcal{L}_3$ is 3 and for $k\geq 4$ the minimum distance of $\mathcal{L}_k$ is 4. The code $\mathcal{L}_k ^{+}$, the minimum distances are respectively 5 and 6 for $ k = 4 $ and $ k \geq 5 $. By calculating the complexity of the algorithms, our codes have fast and efficient decoding. Then, for a short and medium distance data transmission (wifi network, bluetooth, cable, ...), we see that the codes mentioned above present many advantages.

Quantum secure direct communication (QSDC) is a branch of quantum cryptography which exploits laws of physics and was recently proposed for securing information transferred from sender to receiver without relying on computational complexity. Instead of an entangled photon, the quantum cryptography employs two other approaches, single photon and multiphoton for encoding the messages. The multiphoton was proposed to outperform the weaknesses of a single photon by introducing the multiplicity of a photon to perform data encryption tasks. Despite the advantages of multiphoton, the transmission time to transfer the encoded data is still regarded as a critical issue. Most of the multiphoton approaches require multiple photons to travel along a number of stages. Moreover, extra time is needed to change the polarization angle of the optical device for encoding purposes. These situations have resulted in an increase in source redundancy, which subsequently leads to an increase in the transmission time. In this paper, a compression message approach is proposed to mitigate the substantial increase in time used for the data transmission processes. The proposed compression approach, namely, hybrid M-ary in a braided single stage (HMBSS) is based on the lossless data encoding such that the number of photons is reduced in the data transmission stage. Extensive simulation experiments have been conducted to test the performance of the proposed approach against some selected multiphoton approaches. The results show that the proposed approach outperforms braided single-stage, M-ary three-stage and initialization vector in terms of reducing total average transmission time to encode the photon by about 75.9% and 91.7%, respectively. Furthermore, the HMBSS reduces the overhead on the transmission channel by minimizing the number of message's bits by about 45% in average even when the length of the message increases during the transmission as compared to others.

A novel compression-based toolkit for modelling and processing natural language text is described. The design of the toolkit adopts an encoding perspective—applications are considered to be problems in searching for the best encoding of different transformations of the source text into the target text. This paper describes a two phase ‘noiseless channel model’ architecture that underpins the toolkit which models the text processing as a lossless communication down a noise-free channel. The transformation and encoding that is performed in the first phase must be both lossless and reversible. The role of the verification and decoding second phase is to verify the correctness of the communication of the target text that is produced by the application. This paper argues that this encoding approach has several advantages over the decoding approach of the standard noisy channel model. The concepts abstracted by the toolkit’s design are explained together with details of the library calls. The pseudo-code for a number of algorithms is also described for the applications that the toolkit implements including encoding, decoding, classification, training (model building), parallel sentence alignment, word segmentation and language segmentation. Some experimental results, implementation details, memory usage and execution speeds are also discussed for these applications.

In this paper, an efficient image compression algorithm for Bayer-pattern images is introduced. In Bayer pattern images each pixel is represented by 8-bit rather than 24-bit for colour images. The proposed algorithm separates the colour information from the Bayer-pattern image data before performing the compression operation. It partitions each colour plane into fixed size blocks. Each block is classified as a low or high intensity variation block using the standard deviation which compared against a predefined threshold. The low intensity variation blocks are encoded by its average while, the other blocks are encoded using the minimum, quantisation step and the bit plane. The quantisers that used in this paper are from 32 and 16 levels types. The effect of the block size on the output bit rate and image accuracy is studied. The results clarify that a good performance is achieved using 4×4-pixel blocks. Using 32 and 16 quantisation levels a bit rates of 1.8 to 5.2 bpp with a PSNR of 30 to 45 dB can be achieved for a range of thresholds 10 to 0.5.

We introduce gaps that are edges or external pointers in AVL trees such that the height difference between the subtrees rooted at their two endpoints is equal to 2. Using gaps we prove the Basic-Theorem that illustrates how the size of an AVL tree (and its subtrees) can be represented by a series of powers of 2 of the heights of the gaps, this theorem is the first such simple formula to characterize the number of nodes in an AVL tree. Then, we study the extreme case of AVL trees, the perfectly unbalanced AVL trees, by introducing Fibonacci-isomorphic trees that are isomorphic to Fibonacci trees of the same height and showing that they have the maximum number of gaps in AVL trees. Note that two ordered trees (such as AVL trees) are isomorphic iff there exists a one-to-one correspondence between their nodes that preserves not only adjacency relations in the trees, but also the roots. In the rest of the paper, we study combinatorial properties of Fibonacci-isomorphic trees.

Adaptive search is one of the fastest fractal compression algorithms and has gained great success in many industrial applications. By substituting the luminance offset by the range block mean, the authors create a completely new version for both the encoding and decoding algorithms. In this paper, theoretically, they prove that the proposed decoding algorithm converges at least as fast as the existing decoding algorithms using the luminance offset. In addition, they prove that the attractor of the decoding algorithm can be represented by a linear combination of range-averaged images. These theorems are very important contributions to the theory and applications of fractal image compression. As a result, the decoding image can be represented as the sum of the DC and AC component images, which is similar with discrete cosine transform or wavelet transform. To further speed up this algorithm and reduce the complexity of range and domain blocks matching, they propose two improvements in this paper, that is, employing the post-quantisation and geometric neighbouring local search to replace the currently used pre-quantisation and the global search, respectively. The corresponding experimental results show the proposed encoding and decoding algorithms can provide a better performance compared with the existing algorithms.

It has long been known that one of the most effective study techniques is to be tested on the to-be-remembered material, a phenomenon known as the testing effect. Recent research has also shown that testing of previous materials promotes the learning of new materials, a phenomenon known as the forward testing effect. In this paper, as of yet unexplored aspects of the forward testing effect related to face-name learning are examined; continuous and initial testing are compared to restudying, the effects of an initial test on subsequent learning, and whether an initial change of domain (change from one topic to another) regarding study material affects the robustness of the effect. An experiment (N = 94) was performed according to a 2 (Material: word pairs/face-name pairs in Block 1) × 3 (Test occasions: Blocks 1–4/Blocks 1 and 4/Block 4) complex between-groups design. The results showed that no difference between testing and repetition could be observed regarding the recall of faces and names. The restudy groups incorrectly recalled more names from previous lists in the last interim test compared to the tested groups, which supports the theory that interim tests reduce proactive interference. The results also suggest that the number of test occasions correlates with the number of incorrect recalls from previous lists. These results, in contrast to previous studies, highlight a potential uncertainty about the forward testing effect linked to the robustness of the phenomenon, the specificity in execution, and generalizability.

Maximising profit is an important target for industries in a competitive world and it is possible to achieve this by improving the system availability. Engineers have employed many techniques to improve systems availability, such as adding redundant devices or scheduling maintenance strategies. However, the idea of using such techniques simultaneously has not received enough attention. The authors of the present paper recently studied the simultaneous optimisation of system design and maintenance strategy in order to achieve both maximum availability and minimum cost: the Non-dominated Sorting Genetic Algorithm II (NSGA-II) was coupled with Discrete Event Simulation in a real encoding environment in order to achieve a set of non-dominated solutions. In this work, that study is extended and a thorough exploration using the above-mentioned Multi-objective Evolutionary Algorithm is developed using an industrial case study, paying attention to the possible impact on solutions as a result of different encodings, parameter configurations and chromosome lengths, which affect the accuracy levels when scheduling preventive maintenance. Non-significant differences were observed in the experimental results, which raises interesting conclusions regarding flexibility in the preventive maintenance strategy.

John Keats, a main figure in the second generation of Romantic poets, was not generally well received by his contemporary critics, though during the course of time, he has become one of the most beloved poets. Stuart Hall proposes an analytical model of communication, namely the encoding / decoding model, which assumes a complex structure of relations to be produced and sustained through linked but distinctive moments which are termed as production, circulation, distribution/ consumption, and reproduction. This paper employs Hall’s encoding / decoding communication model as a yardstick to move beyond his approach, which mainly addresses modern mass media and communication system, and relate the distinctive moments playing integrally in encoding and decoding to Keats’s Ode on a Grecian Urn (1819). Furthermore, there is an attempt to turn the spotlight on the ode’s durability after the French Revolution passions abate and the poem starts to gain its thingness.

This paper summarizes how the Anglo-Norman Dictionary project has developed from an originally paper-based and print dictionary, to a fully digital, freely available online resource, comprising not only the Dictionary itself, but a substantial text-base in the same XML format as the Dictionary. The paper reviews the history of the Dictionary; the principles of structured encoding; the relationship between the citations (attestations) in the Dictionary, as instances of parole, and the broader Anglo-Norman language (the langue); and the detailed encoding of entries.

Abstract: Lexicographers increasingly acknowledge the enormous potential of electronic dictionaries. The great capacity and speed characteristic of electronic products, combined with enhanced query and data retrieval technology, pave the way to a new generation of dictionaries unimagined in the paper-dictionary era. It is amazing to see how many of the lexicographer's greatest obstacles disappear in the electronic dictionary. This article will, firstly, attempt to give a perspective on typical features of electronic dictionaries. Secondly, electronic-dictionary entries will be designed as a solution to some of the most burning lemmatization problems encountered by lexicographers for African languages in paper dictionaries.Keywords: ELECTRONIC DICTIONARY, LEXICOGRAPHY, DATA RETRIEVAL, LEMMATIZATION, CD-ROM, ACCESS ROUTE, POP-UP FUNCTIONALITIES, POP-UP SCREENS, EDUTAINMENT, CROSS-REFERENCING, INFORMATION RETRIEVAL, ENCODING, DECODING, AFRICAN LANGUAGES, NAVIGATION BAR.Opsomming: Die samestelling van elektroniese woordeboeke vir dieAfrikatale. Leksikograwe erken in toenemende mate die enorme moontlikhede van elektroniesewoordeboeke. Die groot vermoë en spoed wat kenmerkend is van elektroniese produkte,tesame met die bykomende tegnologie van soektogte en dataopsporing, berei die weg voor na 'nnuwe generasie woordeboeke wat ondenkbaar was in die era van gedrukte woordeboeke. Dit isverbasend om te sien hoeveel van die leksikograaf se grootste struikelblokke verdwyn in die elektroniesewoordeboek. Hierdie artikel sal eerstens probeer om 'n oorsig te gee oor die kenmerkendeeienskappe van elektroniese woordeboeke. Tweedens sal inskrywings vir die elektroniese woordeboekontwerp word as oplossing vir sommige van die dringendste lemmatiseringsprobleme watleksikograwe van Afrikatale by gedrukte woordeboeke teëkom.Sleutelwoorde: ELEKTRONIESE WOORDEBOEK, LEKSIKOGRAFIE, DATAOPSPORING,LEMMATISERING, CD-ROM, TOEGANGSROETE, OPSPRINGFUNKSIONALITEITE, OPSPRINGSKERMS,OPVOEDKUNDIGE VERMAAK, KRUISVERWYSING, INLIGTINGSHERWINNING,ENKODERING, DEKODERING, AFRIKATALE, NAVIGASIESTAAF.

Abstract: In this paper the writer examines problems the African Languages Lexical (ALLEX) Project (at present the African Languages Research Institute (ALRI? encountered while tagging the Shona corpus. The problems to be highlighted include general problems which apply to more than one language as well as problems peculiar to Shona. The paper was inspired by the challenges the writer encountered when he took part in building the Shona corpus. An analysis of the problems that most corpus builders face shows that more problems are likely to be encountered when dealing with spoken corpora than with written corpora. The paper demonstrates that tagging is an important component of corpus building as it makes it easier for a researcher to extract relevant data. To utilise the benefits of a tagged corpus, the tagging should be thorough and accurate. Wellinformed decisions form an integral part of the tagging process since the utility of a tagged corpus depends largely on the input of the tagging process. This paper shows the need to take the tagging process seriously.Keywords: ALLEX PROJECT, COMPUTER, CORPUS, ENCODING, FOREIGN WORD, LEMMATIZATION, LEXICOGRAPHY, MONITOR CORPUS, PART OF SPEECH, SCANNING, SHONA, SLANG, TAGGING, TRANSCRIPTION, WORDOpsomming: Die Shonakorpus en die probleem van etikettering, In hierdieartikel ondersoek die outeur probleme wat die African Languages Lexical (ALLEX) Project (tansdie African Languages Research Institute (ALRI» teegekom het terwyl die Shonakorpus geetiketteeris. Die probleme wat bespreek word, sluit algemene probleme in wat van toepassing is opmeer as een taa, sowel as spesifieke probleme wat eie aan Shona is. Die artikel het sy ontstaan indie uitdagings wat die outeur teegekom het terwyl hy deel gehad het aan die opbou van die Shonakorpus.'n Ontieding van die probleme waarvoor die meeste korpusbouers te staan kom, toon datdaar waarskynlik meer probleme teegekom word wanneer daar met gesproke korpora as metgeskrewe korpora gewerk word. Die artikel toon dat etikettering 'n belangrike komponent van korpusbouis, aangesien dit dit vir die navorser makliker maak om relevante data te onttrek. Om dievoordele van korpusetikettering te realiseer, moet die etikettering deeglik en akkuraat wees. Ingeligtebesluite vonn 'n integrale deel van die etiketteringsproses aangesien die bruikbaarheid van 'ngeetiketteerde korpus hoofsaaklik afhang van die inset tydens die etiketteringsproses. Hierdie artikeltoon die noodsaaklikheid om die etiketteringsproses ernstig op te neem.Keywords: ALLEXPROJEK, REKENAAR, KORPUS, ENKODERING, VREEMDE WOORD,LEMMATISERING, LEKSIKOGRAFIE, MONITORKORPUS, WOORDSOORT, SKANDERING,SHONA, SLENG, ETIKETIERING, TRANSKRIPSIE, WOORD

John Keats, a main figure in the second generation of Romantic poets, was not generally well received by his contemporary critics, though during the course of time, he has become one of the most beloved poets. Stuart Hall proposes an analytical model of communication, namely the encoding/decoding model, which assumes a complex structure of relations to be produced and sustained through linked but distinctive moments which are termed as production, circulation, distribution/ consumption, and reproduction. This paper employs Hall’s encoding/decoding communication model as a yardstick to move beyond his approach, which mainly addresses modern mass media and communication system, and relate the distinctive moments playing integrally in encoding and decoding to Keats’s Ode on a Grecian Urn (1819). Furthermore, there is an attempt to turn the spotlight on the ode’s durability after the French Revolution passions abate and the poem starts to gain its thingness.

The rapid growth in the amount of data in the digital world leads to the need for data compression, and so forth, reducing the number of bits needed to represent a text file, an image, audio, or video content. Compressing data saves storage capacity and speeds up data transmission. In this paper, we focus on the text compression and provide a comparison of algorithms (in particular, entropy-based arithmetic and dictionary-based Lempel–Ziv–Welch (LZW) methods) for text compression in different languages (Croatian, Finnish, Hungarian, Czech, Italian, French, German, and English). The main goal is to answer a question: ”How does the language of a text affect the compression ratio?” The results indicated that the compression ratio is affected by the size of the language alphabet, and size or type of the text. For example, The European Green Deal was compressed by 75.79%, 76.17%, 77.33%, 76.84%, 73.25%, 74.63%, 75.14%, and 74.51% using the LZW algorithm, and by 72.54%, 71.47%, 72.87%, 73.43%, 69.62%, 69.94%, 72.42% and 72% using the arithmetic algorithm for the English, German, French, Italian, Czech, Hungarian, Finnish, and Croatian versions, respectively.

This paper discusses the compilation of a Maninka corpus, where the majority of texts are written in the Nko alphabet. Prospects for further development of the Nko corpus are briefly outlined. With this information presented, the aim is to further extend collaborations in collecting texts for the Nko corpus and to improve existing tools for processing Nko texts.

An important problem in visual cryptography is the alignment problem. Although Liu, et al. have proposed a method for aligning the shares, there is still a problem with the non-identical mathematical support visual cryptography schemes. For overcoming this problem, the Three-Orthogonal-Point (3OP) method is proposed in this paper. Based on the experimental result it was proven that it can overcome the alignment problem, while the time complexity for aligning the shares is decreased significantly from O((m×A)2) to O(m×AI), for AI < A. The security is maintained, since an attacker cannot obtain any information related to the secret image.

Production processes in Cellular Manufacturing Systems (CMS) often involve groups of parts sharing the same technological requirements in terms of tooling and setup. The issue of scheduling such parts through a flow-shop production layout is known as the Flow-Shop Group Scheduling (FSGS) problem or, whether setup times are sequence-dependent, the Flow-Shop Sequence-Dependent Group Scheduling (FSDGS) problem. This paper addresses the FSDGS issue, proposing a hybrid metaheuristic procedure integrating features from Genetic Algorithms (GAs) and Biased Random Sampling (BRS) search techniques with the aim of minimizing the total flow time, i.e., the sum of completion times of all jobs. A well-known benchmark of test cases, entailing problems with two, three, and six machines, is employed for both tuning the relevant parameters of the developed procedure and assessing its performances against two metaheuristic algorithms recently presented by literature. The obtained results and a properly arranged ANOVA analysis highlight the superiority of the proposed approach in tackling the scheduling problem under investigation.

Internet world is characterized by many users among which are crackers and thieves. Hence, the need for a secured system to safely exchange confidential information among users across the web is required. Of such tool is steganography that simply hides the user information under other kind of information such as image so that no one suspects that a sensitive data is being transferred. This paper presents a steganography scheme with an improved capacity and enhanced security by compressing the information before embedding it under an image. This is done by encoding the message before embedding it in the blue object of the cover image components (pixels). To prove this scheme, several testing are performed and results are compared.

Biometric system databases are vulnerable to many types of attacks. To address this issue, several biometric template protection systems have been proposed to protect biometric data against unauthorized use. Many of biometric protection systems require the biometric templates to be represented in a binary form. Therefore, extracting binary templates from real-valued biometric data is a key step in such biometric data protection systems. In addition, binary representation of biometric data can speed-up the matching process and reduce the storage capacity required to store the enrolled templates. The main challenge of existing biometric data binarization approaches is to retain the discrimination power of the original real-valued templates after binarization. In this paper, we propose a secure and efficient biometric data binarization scheme that employs multi-objective optimization using Nondominated Sorting Genetic Algorithm (NSGA-II). The goal of the proposed method is to find optimal quantization and encoding parameters that are employed in the binarization process. Results obtained from the experiments conducted on the ORL face and MCYT fingerprint databases show a promising recognition accuracy without sacrificing the security of the system.

Data Compression may be defined as the science and art of the representation of information in a crisply condensed form. For decades, Data compression has been one of the critical enabling technologies for the ongoing digital multimedia revolution. There are a lot of data compression algorithms which are available to compress files of different formats. This paper provides a survey of different basic lossless data compression algorithms. Experimental results and comparisons of the lossless compression algorithms using Statistical compression techniques and Dictionary based compression techniques were performed on text data. Among the Statistical coding techniques, the algorithms such as Shannon-Fano Coding, Huffman coding, Adaptive Huffman coding, Run Length Encoding and Arithmetic coding are considered. Lempel Ziv scheme which is a dictionary based technique is divided into two families: one derived from LZ77 (LZ77, LZSS, LZH, LZB and LZR) and the other derived from LZ78 (LZ78, LZW, LZFG, LZC and LZT). A set of interesting conclusions are derived on this basis.

The eXtensible Markup Language (XML) is a format that is widely used as a tool for data exchange and storage. It is being increasingly used in secure transmission of image data over wireless network and World Wide Web. Verbose in nature, XML files can be tens of megabytes long. Thus, to reduce their size and to allow faster transmission, compression becomes vital. Several general purpose compression tools have been proposed without satisfactory results. This paper proposes a novel technique using modified BWT for compressing XML files in a lossless fashion. The experimental results show that the performance of the proposed technique outperforms both general purpose and XML-specific compressors.

U radu se opisuje jedan model telekomunikacionog kanala, na kojem su primenjene mere kriptozaštite informacija, sa sastavnim elementima kanala. Da bi šifrovanje informacija bilo uspešno na celom spojnom putu neophodno je razmotriti neke osnovne odredbe telekomunikacione i kriptološke sinhronizacije i delimično naglasiti način šifrovanja informacije, kao i proces generisanja i distribucije kriptoloških ključeva. / According to predefined concepts as a basic provision of telecommunications, encryption synchronization and communication threats, it is possible to determine terms for a safe commercial communication channel. The basis of this work is a model of a telecommunication channel with its elements, with encryption and without it. Pursuant to this model, this paper examines the problems of encrypting commercial communication channels. The concept of encryption synchronization, realized by different types of synchronization, is discussed.

In this paper, an encoding strategy is used to design specialized fiber Bragg grating (FBG) sensors. The encoding of each sensor requires two binary codewords to define the amplitude and phase patterns of each sensor. The combined pattern (amplitude and phase) makes each sensor unique and therefore two or more sensors can be identified under spectral overlapping. In this way, we add another dimension to the multiplexing of FBG sensors, obtaining an increase factor ‘n’ to enhance the number of sensors that the system can handle. A proof-of-concept scenario with three sensors was performed, including the manufacturing of the encoded sensors. Furthermore, an interrogation setup to detect the sensors central wavelength was proposed and its working principle was theoretically developed. Results show that total identification of the central wavelength is performed under spectral overlapping between the manufactured sensors, achieving a three-time improvement of the system capacity. Finally, the error due to overlapping between the sensors was assessed obtaining approximately 3 pm, which makes the approach suitable for use in real measurement systems.

Suppression of cholinergic receptors and inactivation of the septum impair short-term memory, and disrupt place cell and grid cell activity in the medial temporal lobe (MTL). Location-dependent hippocampal place cell firing during active waking, when the acetylcholine level is high, switches to time-compressed replay activity during quiet waking and slow-wave-sleep, when the acetylcholine level is low. However, it remains largely unknown how acetylcholine supports short-term memory, spatial navigation, and the functional switch to replay mode in the MTL. In this paper, we focus on the role of the calcium activated non-specific cationic (CAN) current which is activated by acetylcholine. The CAN current is known to underlie persistent firing, which could serve as a memory trace in many neurons in the MTL. Here, we review the CAN current and discuss possible roles of the CAN current in short-term memory and spatial navigation. We further propose a novel theoretical model where the CAN current switches the hippocampal place cell activity between real-time and time-compressed sequential activity during encoding and consolidation, respectively.

This paper presents an encoding scheme adapted for Fiber Bragg Grating (FBG) optimization using metaheuristics. The proposed encoding scheme uses spline approximations in order to build softened refractive index profiles from few encoded parameters. This approach is suitable for Fiber Bragg Grating (FBG) synthesis because it ensures both the reduction of the problem dimensionality and the respect of important restrictions associated to the FBG manufacture. Simulations are shown where an ES using the spline encoding was able to converge faster and produce more interesting filters, when compared with conventional encoding schemes.