Side Information Sets Research Articles

Quantum Representation based Preference Evolution Network for E-commerce recommendation

Quantum theory, originally developed to explain microscopic physical systems, has recently emerged as a novel conceptual and mathematical framework in information science. This paper applies quantum theory to address challenges in E-commerce recommendation, specifically those involving sequential behavior, aiming to mine effective patterns of preference evolution and more accurately predict user interests. Current recommender systems are limited by the sequence length and underutilize side information such as item attributes. To address these issues, we propose a Quantum Representation-based Preference Evolution Network (QRPEN) for E-commerce recommendations. Unlike traditional methods that focus solely on item-ID, our approach integrates a comprehensive set of side information, including both item-ID and attribute data, at each timestamp. We represent item attributes using quantum superposition states and employ density matrices to describe the probability distribution of same-type attributes. These matrices are then transformed into vectors through a quantum measurement-inspired process and fed into a Quasi-RNN model, enabling parallelization and the modeling of longer sequences. This approach effectively captures the dynamic evolution of user preferences. Experiments on public E-commerce datasets demonstrate that QRPEN achieves competitive performance.

Index Coding with Multiple Interpretations.

The index coding problem consists of a system with a server and multiple receivers with different side information and demand sets, connected by a noiseless broadcast channel. The server knows the side information available to the receivers. The objective is to design an encoding scheme that enables all receivers to decode their demanded messages with a minimum number of transmissions, referred to as an index code length. The problem of finding the minimum length index code that enables all receivers to correct a specific number of errors has also been studied. This work establishes a connection between index coding and error-correcting codes with multiple interpretations from the tree construction of nested cyclic codes. The notion of multiple interpretations using nested codes is as follows: different data packets are independently encoded, and then combined by addition and transmitted as a single codeword, minimizing the number of channel uses and offering error protection. The resulting packet can be decoded and interpreted in different ways, increasing the error correction capability, depending on the amount of side information available at each receiver. Motivating applications are network downlink transmissions, information retrieval from datacenters, cache management, and sensor networks.

Embedded Index Coding With Consecutive Side Information in Vehicle to Vehicle Communication

Embedded index coding problem is a variant of index coding problems, where each user functions as both broadcast sender and receiver. Consider a set of the messages, the indices of which form an ordered set. Each user owns a subset of these messages known as side information and tries to meet the demands of one another with a minimum number of transmissions. The side information is said to be consecutive if the elements of the index set of side information at a user are consecutive. We consider a practical vehicular ad-hoc network where the embedded index coding could be applied. In the scenario considered, the receiver demands all messages it does not possess, and the side-information is consecutive. Under this setup, a lower bound on the number of transmissions is determined. We present an optimal solution to achieve the bound. The constructed codes work over any field size, including binary.

Tight Information Theoretic Converse Results for Some Pliable Index Coding Problems

This paper studies the Pliable Index CODing problem (PICOD), which models content-type distribution networks. In the PICOD$(t)$ problem there are $m$ messages, $n$ users and each user has a distinct message side information set, as in the classical Index Coding problem (IC). Differently from IC, where each user has a pre-specified set of messages to decode, in the PICOD$(t)$ a user is "pliable" and is satisfied if it can decode any $t$ messages that are not in its side information set. The goal is to find a code with the shortest length that satisfies all the users. This flexibility in determining the desired message sets makes the PICOD$(t)$ behave quite differently compared to the IC, and its analysis challenging. This paper mainly focuses on the \emph{complete--$S$} PICOD$(t)$ with $m$ messages, where the set $S\subset[m]$ contains the sizes of the side information sets, and the number of users is $n=\sum_{s\in S}\binom{m}{s}$, with no two users having the same side information set. Capacity results are shown for: (i) the \emph{consecutive} complete--$S$ PICOD$(t)$, where $S=[s_{\min}:s_{\max}]$ for some $0 \leq s_{\min} \leq s_{\max} \leq m-t$, and (ii) the \emph{complement-consecutive} complete--$S$ PICOD$(t)$, where $S=[0:m-t]\backslash[s_{\min}:s_{\max}]$, for some $0 < s_{\min} \leq s_{\max} < m-t$. The novel converse proof is inspired by combinatorial design techniques and the key insight is to consider all messages that a user can eventually decode successfully, even those in excess of the $t$ required ones. This allows one to circumvent the need to consider all possible desired message set assignments at the users in order to find the one that leads to the shortest code length. In addition, tight converse results are also shown for those PICOD$(1)$ with circular-arc network topology hypergraph.

StegoAppDB: a Steganography Apps Forensics Image Database

In this paper, we present a new reference dataset simulating digital evidence for image steganography. Steganography detection is a digital image forensic topic that is relatively unknown in practical forensics, although stego app use in the wild is on the rise. This paper introduces the first database consisting of mobile phone photographs and stego images produced from mobile stego apps, including a rich set of side information, offering simulated digital evidence. StegoAppDB, a steganography apps forensics image database, contains over 810,000 innocent and stego images using a minimum of 10 different phone models from 24 distinct devices, with detailed provenanced data comprising a wide range of ISO and exposure settings, EXIF data, message information, embedding rates, etc. We develop a camera app, Cameraw, specifically for data acquisition, with multiple images per scene, saving simultaneously in both DNG and high-quality JPEG formats. Stego images are created from these original images using selected mobile stego apps through a careful process of reverse engineering. StegoAppDB contains cover-stego image pairs including for apps that resize the stego dimensions. We retainthe original devices and continue to enlarge the database, and encourage the image forensics community to use StegoAppDB. While designed for steganography, we discuss uses of this publicly available database to other digital image forensic topics.

Robust Inductive Matrix Completion Strategy to Explore Associations Between LincRNAs and Human Disease Phenotypes.

Over the past few years, it has been established that a number of long intergenic non-coding RNAs (lincRNAs) are linked to a wide variety of human diseases. The relationship among many other lincRNAs still remains as puzzle. Validation of such link between the two entities through biological experiments is expensive. However, piles of information about the two are becoming available, thanks to the High Throughput Sequencing (HTS) platforms, Genome Wide Association Studies (GWAS), etc., thereby opening opportunity for cutting-edge machine learning and data mining approaches. However, there are only a few in silico lincRNA-disease association inference tools available to date, and none of these utilizes side information of both the entities. The recently developed Inductive Matrix Completion (IMC) technique provides a recommendation platform among two entities considering respective side information. But, the formulation of IMC is incapable of handling noise and outliers that may present in the dataset, while data sparsity consideration is another issue with the standard IMC method. Thus, a robust version of IMC is needed that can solve these two issues. As a remedy, in this paper, we propose Robust Inductive Matrix Completion (RIMC) using l2,1 norm loss function as well as l2,1 norm based regularization. We applied RIMC to the available association data between human lincRNAs and OMIM disease phenotypes as well as a diverse set of side information about the lincRNAs and the diseases. Our method performs better than the state-of-the-art methods in terms of precision@k and recall@k at the top- k disease prioritization to the subject lincRNAs. We also demonstrate that RIMC is equally effective for querying about novel lincRNAs, as well as predicting rank of a newly known disease for a set of well-characterized lincRNAs. Availability: All the supporting datasets are available at the publicly accessible URL located at http://biomecis.uta.edu/~ashis/res/RIMC/.

Stable solution to l2,1-based robust inductive matrix completion and its application in linking long noncoding RNAs to human diseases

BackgroundsA large number of long intergenic non-coding RNAs (lincRNAs) are linked to a broad spectrum of human diseases. The disease association with many other lincRNAs still remain as puzzle. Validation of such links between the two entities through biological experiments are expensive. However, a plethora lincRNA-data are available now, thanks to the High Throughput Sequencing (HTS) platforms, Genome Wide Association Studies (GWAS), etc, which opens the opportunity for cutting-edge machine learning and data mining approaches to extract meaningful relationships among lincRNAs and diseases. However, there are only a few in silico lincRNA-disease association inference tools available to date, and none of them utilizes side information of both the entities simultaneously in a single framework.MethodsThe recently developed Inductive Matrix Completion (IMC) technique provides a recommendation platform among two entities considering respective side information about them. However, the formulation of IMC is incapable of handling noise and outliers that may be present in the datasets, while data sparsity consideration is another issue with the standard IMC method. Thus, a robust version of IMC is needed that can solve the two issues. As a remedy, in this paper, we propose Stable Robust Inductive Matrix Completion (SRIMC) that utilizes the l2,1 norm based regularization to optimize the objective function with a unique 2-step stable solution approach.ResultsWe applied SRIMC to the available association data between human lincRNAs and OMIM disease phenotypes as well as a diverse set of side information about the lincRNAs and the diseases. The method performs better than the state-of-the-art methods in terms of precision@k and recall@k at the top-k disease prioritization to the subject lincRNAs. We also demonstrate that SRIMC is equally effective for querying about novel lincRNAs, as well as predicting rank of a newly known disease for a set of well-characterized lincRNAs.ConclusionsWith the experimental results and computational evaluation, we show that SRIMC is robust in handling datasets with noise and outliers as well as dealing with novel lincRNAs and disease phenotypes.

Error Correction for Index Coding With Coded Side Information

Index coding is a source coding problem in which a broadcaster seeks to meet the different demands of several users, each of whom is assumed to have some prior information on the data held by the sender. A well-known application is satellite communications, as described in one of the earliest papers on the subject (Birk and Kol, 1998). It is readily seen that if the sender has knowledge of its clients’ requests and their side-information sets, then the number of packet transmissions required to satisfy all users’ demands can be greatly reduced if the data are encoded before sending. The collection of side-information indices as well as the indices of the requested data is described as an instance ${ \mathcal I}$ of the index coding with side-information (ICSI) problem. The encoding function is called the index code of $ \mathcal I$ , and the number of transmissions, resulting from the encoding is referred to as its length . The main ICSI problem is to determine the optimal length of an index code and instance $ \mathcal I$ . As this number is hard to compute, bounds approximating it are sought, as are algorithms to compute efficient index codes. These questions have been addressed by several authors (e.g., see Alon et al. 2008, Bar-Yossef et al. 2011, Blasiak et al. 2013), often taking a graph-theoretic approach. Two interesting generalizations of the problem that have appeared in the literature are the subject of this paper. The first of these is the case of index coding with coded side information (Dai et al. 2014), in which linear combinations of the source data are both requested by and held as users’ side-information. This generalization has applications, for example, to relay channels and necessitates algebraic rather than combinatorial methods. The second is the introduction of error-correction in the problem, in which the broadcast channel is subject to noise (Dau et al. 2013). In this paper, we characterize the optimal length of a scalar or vector linear index code with coded side information (ICCSI) over a finite field in terms of a generalized min-rank and give bounds on this number based on constructions of random codes for an arbitrary instance. We furthermore consider the length of an optimal $\delta $ -error correcting code for an instance of the ICCSI problem and obtain bounds analogous to those described in (Dau et al. 2013), both for the Hamming metric and for rank-metric errors. We describe decoding algorithms for both categories of errors.

Information-Theoretic Approach to Strategic Communication as a Hierarchical Game

This paper analyzes the information disclosure problems originated in economics through the lens of information theory. Such problems are radically different from the conventional communication paradigms in information theory since they involve different objectives for the encoder and the decoder, which are aware of this mismatch and act accordingly. This leads, in our setting, to a hierarchical communication game, where the transmitter announces an encoding strategy with full commitment, and its distortion measure depends on a private information sequence whose realization is available at the transmitter. The receiver decides on its decoding strategy that minimizes its own distortion based on the announced encoding map and the statistics. Three problem settings are considered, focusing on the quadratic distortion measures, and jointly Gaussian source and private information: compression, communication, and the simple equilibrium conditions without any compression or communication. The equilibrium strategies and associated costs are characterized. The analysis is then extended to the receiver side information setting and the major changes in structure of optimal strategies are identified. Finally, an extension of the results to the broader context of decentralized stochastic control is presented.

Merge Frame Design for Video Stream Switching Using Piecewise Constant Functions.

The ability to efficiently switch from one pre-encoded video stream to another (e.g., for bitrate adaptation or view switching) is important for many interactive streaming applications. Recently, stream-switching mechanisms based on distributed source coding (DSC) have been proposed. In order to reduce the overall transmission rate, these approaches provide a merge mechanism, where information is sent to the decoder, such that the exact same frame can be reconstructed given that any one of a known set of side information (SI) frames is available at the decoder (e.g., each SI frame may correspond to a different stream from which we are switching). However, the use of bit-plane coding and channel coding in many DSC approaches leads to complex coding and decoding. In this paper, we propose an alternative approach for merging multiple SI frames, using a piecewise constant (PWC) function as the merge operator. In our approach, for each block to be reconstructed, a series of parameters of these PWC merge functions are transmitted in order to guarantee identical reconstruction given the known SI blocks. We consider two different scenarios. In the first case, a target frame is first given, and then merge parameters are chosen, so that this frame can be reconstructed exactly at the decoder. In contrast, in the second scenario, the reconstructed frame and the merge parameters are jointly optimized to meet a rate-distortion criteria. Experiments show that for both scenarios, our proposed merge techniques can outperform both a recent approach based on DSC and the SP-frame approach in H.264, in terms of compression efficiency and decoder complexity.

Pliable Index Coding

We formulate a new variant of the index coding problem, where instead of demanding a specific message, clients are pliable, and are interested in receiving any $t$ messages that they do not have. We term this problem pliable index coding or PICOD $(t)$ . We prove that, with this formulation, although some instances of the problem become simple, in general, the problem of finding the optimal linear code remains NP-hard. However, we show that it is possible to construct pliable index codes that are substantially smaller than index codes in many cases. If there are $n$ clients, the server has $m$ messages, and each client has a side information set of cardinality $s\leq m-t$ ; we show that $O(\min \{t\log n, t+\log ^{2} n\})$ broadcast transmissions are sufficient to satisfy all the clients. For $t=1$ , this is an exponential improvement over the $n$ messages required in index coding in the worst case (for $m=n$ ). In addition, for $t\gg \log ^{2} n$ , the number of broadcast transmissions required is only linearly dependent on $t$ . We generalize the results to instances where the side information sets are not necessarily of equal cardinality. When $m = O(n^\delta )$ , for some constant $\delta >0$ , we show that the codes of size $O(\min \{t\log ^{2} n,t\log n+\log ^{3} n\})$ are sufficient in general. We also consider the scenario when the server only knows the cardinality of the side information sets of the clients and each client is interested in receiving any $t$ messages that it does not have. We term this formulation oblivious pliable index coding or OB-PICOD $(t)$ . If the cardinalities of side information sets of all the clients is $s$ (with $s\leq m-t$ ), then we show that $\min \{s+t,m-s\}$ messages are both sufficient and necessary for linear codes. Finally, we develop efficient heuristic approximation algorithms for PICOD $(t)$ and show through simulations on the random instances of PICOD $(t)$ that they perform well in practice.

A Distributed Video Coding Algorithm Based on Weighted Motion Estimation

The RD performance of distributed video coding (DVC) is lower than conventional video coding. This article analyzes the reason of that, finds during the decoding process DVC does not make full use of the set of side information in the distributed framework. Then paper proposes a way to improve the prediction accuracy, during the process of generating predictive side information, makes full use of the distributed structural characteristics, improves the RD performance of DVC by using bidirectional motion estimation and the weighted average methods

Distributed Video Coding with Multiple Side Information Sets

This letter proposes a method to retrieve the original image X out of multiple sets of SI (Side Information) in distributed video coding (DVC). Using Turbo decoding methods, the most reliable segments from the decoded Yi's were selected for the composition of Y∞, whose conditional entropy H(X|Y∞) became much lower than any individual conditional entropy H(X|Yi). This proposal has improved the peak signal-to-noise ratio (PSNR) by 1.1 to 1.8dB, compared to the conventional single SI-based approach.

Source Coding With Limited-Look-Ahead Side Information at the Decoder

We characterize the rate distortion function for the source coding with decoder side information setting when the ith reconstruction symbol is allowed to depend only on the first i+lscr side information symbols, for some finite look-ahead lscr, in addition to the index from the encoder. For the case of causal side information, i.e., lscr=0, we find that the penalty of causality is the omission of the subtracted mutual information term in the Wyner-Ziv rate distortion function. For lscr>0, we derive a computable "infinite-letter" expression for the rate distortion function. When specialized to the near-lossless case, our results characterize the best achievable rate for the Slepian-Wolf source coding problem with finite side information looka-head, and have some surprising implications. We find that side information is useless for any fixed lscr when the joint probability mass function (PMF) of the source and side information satisfies the positivity condition P(x,y)>0 for all (x,y). More generally, the optimal rate depends on the distribution of the pair X,Y only through the distribution of X and the bipartite graph whose edges represent the pairs x,y for which P(x,y)>0. On the other hand, if side information look-ahead is allowed to grow faster than logarithmic in the block length, then H(X|Y) is achievable. Finally, we apply our approach to derive a computable expression for channel capacity when state information is available at the encoder with limited look-ahead.

Blind Scheme with Histogram Peak Estimation for Histogram Modication-Based Lossless Information Embedding

In this paper, a simple scheme for histogram modification-based lossless information embedding (HM-LIE) is proposed. The proposed scheme is free from memorizing side information, i.e., blind. A HMLIE scheme modifies particular pixel values in an image in order to embed information in it on the basis of its histogram, i.e., tonal distribution. The scheme recovers the original image as well as extracts embedded information from a distorted image conveying embedded information. Most HM-LIE schemes should memorize a set of image-dependent side information per image. The proposed scheme does not have to memorize such information to avoid costly identification of the distorted image carrying embedded information because of the introduction of two mechanisms. One is estimating side information on the basis of a simple statistic, and the other is concealing not only main information but also a part of the side information in the image. These approaches make the proposed scheme superior to the conventional blind schemes in terms of the quality of imagesconveying embedded information.