In many real-world applications, labeled training data is hard to come by for text classification. These tasks are often domain specific, where the vocabulary of the textual input is different than that of the general language vocabulary. In this paper, we deal with one of such tasks of automation of a software monitoring system, where logs are analyzed in real-time. We describe a weakly supervised method to process incoming streams of logs for identifying fault types in logs. We propose hand-crafted feature extractions, specially designed for the classifiers for log inputs. In order to make the processing time efficient and generalizable across various log sources, we rely on a weak supervised fault classifier, where the domain knowledge is incorporated using a word embedding mode built on a domain specific corpus. Experiments on logs obtained from various applications show the efficacy of our proposed method.

A Bitcoin Generator Scam (BGS) is a type of cyberattack in which scammers promise to provide individuals with free cryptocurrencies if they pay a mining fee. Although graph neural networks (GNNs) have been used for detecting other cryptocurrency frauds, the usefulness of these methods for BGS detection has not been studied. In this paper, we carry out extensive experiments to assess the use of both standard machine learning (ML) methods and GNNs to detect Bitcoin transactions associated with activities stemming from Bitcoin Generator Scams. We observe that the over-smoothing problem exists in GNNs designed for BGS detection and show that Random Walk Positional Encoding (RWPE) allows representing long-range interactions between far-away transactions in GNNs without causing over-smoothing. We show that the General, Powerful, Scalable (GPS) Graph Transformer with RWPE outperforms both GNN and ML based state-of-the-art fraud detection methods in Bitcoin Generator Scams. We also analyze the effectiveness of Breadth First Search (BFS) for graph sampling and show that it should not be used as it induces bias toward the subnetwork structure. We propose the Random First Search (RFS) sampling alternative and show that this is a more suitable solution.

Optimal transport is a framework for comparing measures whereby a cost is incurred for transporting one measure to another. Recent works have aimed to improve optimal transport plans through the introduction of various forms of structure. We introduce novel order constraints into the optimal transport formulation to allow for the incorporation of structure. We define an efficient method for obtaining explainable solutions to the new formulation that scales far better than standard approaches. The theoretical properties of the method are provided. We demonstrate experimentally that order constraints improve explainability using the e-SNLI (Stanford Natural Language Inference) dataset that includes human-annotated rationales as well as on several image color transfer examples.

Blockchains are designed for trustworthy and transparent execution of transactions involving multiple parties. An important class of applications requires data to be shared selectively among mutually anonymous transacting peers while retaining the tamper-resistant evidentiary and validation features of a blockchain. KYC validations of corporate customers by banks is one example, where both banks and customers benefit from sharing process and data on a blockchain network. However, sharing of confidential KYC data must be authorized by customers, and a bank-customer relationship must be kept secret from other banks in the network. In this paper, we describe the design and implementation of a smart contract for consent-driven and double-blind data sharing on the Hyperledger Fabric blockchain platform. We show how a KYC application was built around this model to address the needs of the banks while meeting regulatory requirements.

Index coding studies multiterminal source-coding problems where a set of receivers are required to decode multiple (possibly different) messages from a common broadcast, and they each know some messages a priori. In this paper, at the receiver end, we consider a special setting where each receiver knows only one message a priori, and each message is known to only one receiver. At the broadcasting end, we consider a generalized setting where there could be multiple senders, and each sender knows a subset of the messages. The senders collaborate to transmit an index code. This paper looks at minimizing the number of total coded bits the senders are required to transmit. When there is only one sender, we propose a pruning algorithm to find a lower bound on the optimal (i.e., the shortest) index codelength, and show that it is achievable by linear index codes. When there are two or more senders, we propose an appending technique to be used in conjunction with the pruning technique to give a lower bound on the optimal index codelength; we also derive an upper bound based on cyclic codes. While the two bounds do not match in general, for the special case where no two distinct senders know any message in common, the bounds match, giving the optimal index codelength. The results are expressed in terms of strongly connected components in directed graphs that represent the index-coding problems.

We focus on the following instance of an index coding problem, where a set of receivers are required to decode multiple messages, whilst each knows one of the messages a priori. In particular, here we consider a generalized setting where they are multiple senders, each sender only knows a subset of messages, and all senders are required to collectively transmit the index code. For a single sender, Ong and Ho (ICC, 2012) have established the optimal index codelength, where the lower bound was obtained using a pruning algorithm. In this paper, the pruning algorithm is simplified, and used in conjunction with an appending technique to give a lower bound to the multi-sender case. An upper bound is derived based on network coding. While the two bounds do not match in general, for the special case where no two senders know any message bit in common, the bounds match, giving the optimal index codelength. The results are derived based on graph theory, and are expressed in terms of strongly connected components.

The max-log-MAP (MLM) receiver is an approximated version of the well-known Bahl-Cocke-Jelinek-Raviv algorithm. The MLM algorithm is attractive due to its implementation simplicity. In practice, sliding-window implementations are preferred, whereby truncated signaling neighborhoods (around each transmission time instant) are considered. In this paper, we consider binary signaling sliding-window MLM receivers, where the MLM detector is truncated to a length-m signaling neighborhood. Here, truncation is used to ease the burden of analysis. For any number n of chosen times instants, we derive exact expressions for both 1) the joint distribution of the MLM symbol reliabilities, and 2) the joint probability of the erroneous MLM symbol detections. We show that the obtained expressions can be efficiently evaluated using Monte-Carlo techniques. The most computationally expensive operation (in each Monte-Carlo trial) is an eigenvalue decomposition of a size 2mn×2mn matrix. The proposed method handles various scenarios such as correlated noise distributions, modulation coding, etc.

This paper evaluates the impact of circuit impairments on the energy cost and performance limitations of analog-to-information converters (AIC). In applications where signal frequencies are high, but information bandwidths are low, AICs have been proposed as a potential solution to overcome the resolution and performance limitations of sampling jitter in high-speed analog-to-digital converters (ADC). Although the AIC architecture facilitates slower ADCs, the signal encoding, typically realized with a mixer-like circuit, still occurs at the Nyquist frequency of the input to avoid aliasing. We show that the jitter of this mixing stage limits the achievable AIC resolution. In this work, the end-to-end system evaluation framework is designed to analyze these limitations as well as the relative energy-efficiency of AICs versus ADCs across the resolution, receiver gain and signal sparsity. The evaluation shows that AICs improve the resolution by 1 bit when the signal of interest is very sparse, and enable 2× in energy savings when no pre-amplification is required.

We apply Heoffding's U-statistics to obtain non-asymptotic analysis for compressed sensing (CS) random matrices. These powerful (U-statistics) tools appear to apply naturally to CS theory, in particular here we focus on one particular large deviation result. We chose two applications to outline how U-statistics may apply to various CS recovery guarantees. Pros, cons, and further directions of the approach, are discussed. Restricted isometries of random matricies have well-regarded importance in CS. They guarantee i) uniqueness of sparse solutions, and ii) robust recovery. The fraction of size-k submatrices (out of all (n/k) of them), that satisfy CS-type restricted isometeries, is an U-statistic. Concentration of U-statistics predict the “average-case” behavior of such isometries. U-statistics related to Fuchs' conditions for ℓ1-minimization support recovery, are derived. This leads to bounds on the fraction of recoverable k-supports. Empirically, we observe significant improvement over a recent large deviation (non-asymptotic) bound by Donoho & Tanner, for some practical system sizes with large undersampling. The results apply regardless of column distribution, e.g. Gaussian, Bernoulli, etc. Similar concentration behavior has been empirically observed, when the sampling matrix is constructed using pseudorandom sequences (important in practice).

Recent interest on permutation rank modulation shows the Kendall-tau metric as an important distance metric. This note documents our first efforts to obtain upper bounds on optimal code sizes (for said metric) ala Delsarte's approach. For the Hamming metric, Delsarte's seminal work on powerful linear programming (LP) bounds have been extended to permutation codes, via association scheme theory. For the Kendall-tau metric, the same extension needs the more general theory of coherent configurations, whereby the optimal code size problem can be formulated as an extremely huge semidefinite programming (SDP) problem. Inspired by recent algebraic techniques for solving SDP's, we consider the dual problem, and propose an LP to search over a subset of dual feasible solutions. We obtain modest improvement over a recent Singleton bound due to Barg and Mazumdar. We regard this work as a starting point, towards fully exploiting the power of Delsarte's method, which are known to give some of the best bounds in the context of binary codes.

In this paper, we consider sliding-window max-log-map (MLM) receivers, where for any integer m, the MLM detector is truncated to a length-m signaling neighborhood. For any number n of chosen time instants, we provide exact expressions for both i) the joint distribution of the MLM symbol reliabilities, and ii) the joint probability of the erroneous MLM symbol detections. The obtained expressions can be efficiently evaluated using Monte-Carlo techniques. Comparisons performed with empirical distributions reveal good match. Dynamic programming techniques are applied to simplify the procedures.

We introduce two permutation decoder designs for the binary images of double-parity [n, n - 2, 3] Reed-Solomon (RS) codes over binary extension fields F2m. The codes considered are limited to have zeros {1, α}, where α is any primitive element in F2m. We show that there exists a large set of m binary symbol errors that may be corrected via permutation decoding. The permutation decoders are shown to achieve near maximum-likelihood decoder performance, while only utilizing simple ideas borrowed from well-known reliability-based decoding algorithms.

For the memoryless AWGN channel, there exists low complexity methods to test the optimality of any chosen candidate codeword (i.e., whether the codeword in question equals the most-likely codeword or not). Such optimality tests find application in practical decoders that perform heuristic searches for the most-likely codeword. If some located codeword passes the optimality test, then the search may be terminated and computations saved. In this paper, we generalize techniques for determining if a codeword is optimal, for intersymbol interference (ISI) channels.

In this paper, we present a generalization of the ordered statistics decoding (OSD) techniques for the class of intersymbol interference (ISI) channels, and show decoding results for the extended Bose-Chaudhuri-Hocquenghem (eBCH) [128, 64, 22] code and the [255, 239, 17] Reed-Solomon (RS) binary image, over the PR2 partial response channel. Using the generalized OSD technique, we go on to generalize the Box-and- Match Algorithm (BMA) to the class of ISI channels. The BMA is an enhancement of OSD, and prior work has shown it to provide significant performance gain over OSD for memoryless additive white Gaussian noise (AWGN) channels. We present decoding results of the BMA for ISI channels, for the same eBCH and RS (binary image) codes, and PR2 channel. Our results show that the BMA (generalized for ISI channels) is superior to the OSD in terms of its performance/complexity trade-off. More specifically, the BMA may be tuned such that both algorithms have similar complexity, whereby the BMA still outperforms the OSD by a significant margin.

We consider primitive Reed-Solomon (RS) codes over the field F2mof length n=2m-1. Building on Lacan 's results for the case of binary extension fields, we show that the binary images of certain two-parity symbol RS [n, n-2, 3] code, have a code automorphism subgroup related to the general linear group GL(m, 2). For these codes, we obtain a code automorphism subgroup of order m! GL(m,2). An explicit algorithm is given to compute a code automorphism (if it exists), that sends a particular choice of m binary positions, into binary positions that correspond to a single symbol of the RS code. If one such automorphism exists for a particular choice of m binary symbol positions, we show that there are at least m! of them. Computationally efficient permutation decoders are designed for the two-parity symbol RS [n, n-2, 3] codes. Simulation results are shown for the additive white Gaussian noise (AWGN) channel. For the finite fields F23and F24, we go on to derive subgroups of code automorphisms, belonging to binary images of certain RS codes that have three-parity symbols. A table of code automorphism subgroup orders, computed using the Groups, Algorithms, and Programming (GAP) software, is tabulated for the fields F23, F24, and F25.

In this paper, a new proof of the work by Lacan et. al is obtained. This approach led to newly discovered connections between the automorphism group of the Reed Solomon (RS) binary image, and elementary group theory. This new development facilitated proving the existence and constructing permutations that have not been previously reported in the literature. Simple special cases are then considered in this work.

When precompensating for partial erasure (PE) together with non-linear transition shift (NLTS) in saturation magnetic recording, it is possible to offset written transitions from the sampling intervals to obtain a more 'linearized' readback signal. The premise of this paper is to show how to compute these optimal precompensation values using dynamic programming, given known functional forms of the PE and NLTS functions.