Lightweight Conceptual Dictionary Learning for Text Classification Using Information Compression

We propose a novel, lightweight supervised dictionary learning framework for text classification based on data compression and representation. This two-phase algorithm initially employs the Lempel-Ziv-Welch (LZW) algorithm to construct a dictionary from text datasets, focusing on the conceptual significance of dictionary elements. Subsequently, dictionaries are refined considering label data, optimizing dictionary atoms to enhance discriminative power based on mutual information and class distribution. This process generates discriminative numerical representations, facilitating the training of simple classifiers such as SVMs and neural networks. We evaluate our algorithm's information-theoretic performance using information bottleneck principles and introduce the information plane area rank (IPAR) as a novel metric to quantify the information-theoretic performance. Tested on six benchmark text datasets, our algorithm competes closely with top models, especially in limited-vocabulary contexts, using significantly fewer parameters. \review{Our algorithm closely matches top-performing models, deviating by only ~2\% on limited-vocabulary datasets, using just 10\% of their parameters. However, it falls short on diverse-vocabulary datasets, likely due to the LZW algorithm's constraints with low-repetition data. This contrast highlights its efficiency and limitations across different dataset types.

OTFS Channel Estimation and Detection for Channels with Very Large Delay Spread

In low latency applications and in general, for overspread channels, channel delay spread is a large percentage of the transmission frame duration. In this paper, we consider OTFS in an overspread channel exhibiting a delay spread that exceeds the block duration in a frame, where traditional channel estimation (CE) fails. We propose a two-stage CE method based on a delay-Doppler (DD) training frame, consisting of a dual chirp converted from time domain and a higher power pilot. The first stage employs a DD domain embedded pilot CE to estimate the aliased delays (due to modulo operation) and Doppler shifts, followed by identifying all the underspread paths not coinciding with any overspread path. The second stage utilizes time domain dual chirp correlation to estimate the actual delays and Doppler shifts of the remaining paths. This stage also resolves ambiguity in estimating delays and Doppler shifts for paths sharing same aliased delay. Furthermore, we present a modified low-complexity maximum ratio combining (MRC) detection algorithm for OTFS in overspread channels. Finally, we evaluate performance of OTFS using the proposed CE and the modified MRC detection in terms of normalized mean square error (NMSE) and bit error rate (BER).

Robust Localization and Tracking of UAVs in OTFS-based Networks

We consider the problem of accurately localizing N unmanned aerial vehicles (UAV) in 3D space where the UAVs are part of a swarm and communicate with each other through orthogonal time-frequency space (OTFS) modulated signals. Each receiving UAV estimates the multipath wireless channel on each link formed by the line-of-sight (LoS) transmission and by the single reflections from the remaining N-2 UAVs. The estimated power delay profiles are communicated to an edge server, which is in charge of computing the exact location and speed of the UAVs. To obtain the UAVs locations and velocities, we propose an iterative algorithm, named Turbo Iterative Positioning (TIP), which, using a belief-propagation approach, effectively exploits the time difference of arrival (TDoA) measurements between the LoS and the non-LoS paths. Enabling a full cold start (no prior knowledge), our solution first maps each TDoA's profile element to a specific ID of the reflecting UAV's. The Doppler shifts measured by the OTFS receivers associated with each path are also used to estimate the UAV's velocities. The localization of the N UAVs is then derived via gradient descent optimization, with the aid of turbo-like iterations that can progressively correct some of the residual errors in the initial ID mapping operation. Our numerical results, obtained also using real-world traces, show how the multipath links are beneficial to achieving very accurate localization and speed of all UAVs, even with a limited delay-Doppler resolution. Robustness of our scheme is proven by its performance approaching the Cramer-Rao bound.

Improving Robustness and Accuracy of Ponzi Scheme Detection on Ethereum Using Time-Dependent Features

The rapid development of blockchain has led to more and more funding pouring into the cryptocurrency market, which also attracted cybercriminals' interest in recent years. The Ponzi scheme, an old-fashioned fraud, is now popular on the blockchain, causing considerable financial losses to many crypto-investors. A few Ponzi detection methods have been proposed in the literature, most of which detect a Ponzi scheme based on its smart contract source code or opcode. The contract-code-based approach, while achieving very high accuracy, is not robust: first, the source codes of a majority of contracts on Ethereum are not available, and second, a Ponzi developer can fool a contract-code-based detection model by obfuscating the opcode or inventing a new profit distribution logic that cannot be detected (since these models were trained on existing Ponzi logics only). A transaction-based approach could improve the robustness of detection because transactions, unlike smart contracts, are harder to be manipulated. However, the current transaction-based detection models achieve fairly low accuracy. We address this gap in the literature by developing new detection models that rely only on the transactions, hence guaranteeing the robustness, and moreover, achieve considerably higher Accuracy, Precision, Recall, and F1-score than existing transaction-based models. This is made possible thanks to the introduction of novel time-dependent features that capture Ponzi behaviours characteristics derived from our comprehensive data analyses on Ponzi and non-Ponzi data from the XBlock-ETH repository

Committed Private Information Retrieval

A private information retrieval (PIR) scheme allows a client to retrieve a data item $x_i$ among $n$ items $x_1,x_2,...,x_n$ from $k$ servers, without revealing what $i$ is even when $t < k$ servers collude and try to learn $i$. Such a PIR scheme is said to be $t$-private. A PIR scheme is $v$-verifiable if the client can verify the correctness of the retrieved $x_i$ even when $v \leq k$ servers collude and try to fool the client by sending manipulated data. Most of the previous works in the literature on PIR assumed that $v < k$, leaving the case of all-colluding servers open. We propose a generic construction that combines a linear map commitment (LMC) and an arbitrary linear PIR scheme to produce a $k$-verifiable PIR scheme, termed a committed PIR scheme. Such a scheme guarantees that even in the worst scenario, when all servers are under the control of an attacker, although the privacy is unavoidably lost, the client won't be fooled into accepting an incorrect $x_i$. We demonstrate the practicality of our proposal by implementing the committed PIR schemes based on the Lai-Malavolta LMC and three well-known PIR schemes using the GMP library and \texttt{blst}, the current fastest C library for elliptic curve pairings.

Low-Complexity Linear Diversity-Combining Detector for MIMO-OTFS

This paper presents a low complexity detector for multiple-input multiple-output (MIMO) systems based on the recently proposed orthogonal time frequency space (OTFS) modulation. In the proposed detector, the copies of the transmitted symbol-vectors received through the different diversity branches (propagation paths and receive antennas) are linearly combined using the maximum ratio combining (MRC) technique to iteratively improve the signal to interference plus noise ratio (SINR) at the output of the combiner. To alleviate the performance degradation due to spatial correlation at the receiver antennas, we present a sample-based method to estimate such correlation and find the optimized combining weights for MRC from the estimated correlation matrix. The detector performance and complexity improve over the linear minimum mean square error (LMMSE) and message passing (MP) detectors proposed in the literature for MIMO-OTFS.

Unitary-Precoded Single-Carrier Waveforms for High Mobility: Detection and Channel Estimation

This paper presents unitary-precoded single-carrier (USC) modulation as a family of waveforms based on multiplexing the information symbols on time domain unitary basis functions. The common property of these basis functions is that they span the entire time and frequency plane. The recently proposed orthogonal time frequency space (OTFS) and orthogonal time sequency multiplexing (OTSM) based on discrete Fourier transform (DFT) and Walsh Hadamard transform (WHT), respectively, fall in the general framework of USC waveforms. In this work, we present channel estimation and detection methods that work for any USC waveform and numerically show that any choice of unitary precoding results in the same error performance. Lastly, we implement some USC systems and compare their performance with OFDM in a real-time indoor setting using an SDR platform.

Orthogonal Time Sequency Multiplexing Modulation: Analysis and Low-Complexity Receiver Design

This paper proposes orthogonal time sequency multiplexing (OTSM), a novel single carrier modulation scheme that places information symbols in the delay-sequency domain followed by a cascade of time-division multiplexing (TDM) and Walsh-Hadamard sequence multiplexing. Thanks to the Walsh Hadamard transform (WHT), the modulation and demodulation do not require complex domain multiplications. For the proposed OTSM, we first derive the input-output relation in the delay-sequency domain and present a low complexity detection method taking advantage of zero-padding. We demonstrate via simulations that OTSM offers high performance gains over orthogonal frequency division multiplexing (OFDM) and similar performance to orthogonal time frequency space (OTFS), but at lower complexity owing to WHT. Then we propose a low complexity time-domain channel estimation method. Finally, we show how to include an outer error control code and a turbo decoder to improve error performance of the coded system.