With the rapidly-growing deployment of large language model (LLM) inference services, privacy concerns have arisen regarding to the user input data.Recent studies are exploring transforming user inputs to obfuscated embedded vectors, so that the data will not be eavesdropped by service provides.However, in this paper we show that again, without a solid and deliberate security design and analysis, such embedded vector obfuscation failed to protect users' privacy.We demonstrate the conclusion via conducting a novel inversion attack called Element-wise Differential Nearest Neighbor (EDNN) on the glide-reflection proposed in (Mishra et al., 2024), and the result showed that the original user input text can be 100% recovered from the obfuscated embedded vectors.We further analyze security requirements on embedding obfuscation and present several remedies to our proposed attack.

Natural language understanding (NLU) aims at identifying user intent and extracting semantic slots.This requires sufficient annotating data to get considerable performance in real-world situations.Active learning (AL) has been well-studied to decrease the needed amount of the annotating data and successfully applied to NLU.However, no research has been done on investigating how the relation information between intents and slots can improve the efficiency of AL algorithms.In this paper, we propose a multitask AL framework for NLU.Our framework enables poolbased AL algorithms to make use of the relation information between sub-tasks provided by a joint model, and we propose an efficient computation for the entropy of a joint model.Experimental results show our framework can achieve competitive performance with less training data than baseline methods on all datasets.We also demonstrate that when using the entropy as the query strategy, the model with complete relation information can perform better than those with partial information.Additionally, we demonstrate that the efficiency of these active learning algorithms in our framework is still effective when incorporate with the Bidirectional Encoder Representations from Transformers (BERT).

In stock markets, an emerging challenge for surveillance is that a group of hidden manipulators collaborate with each other to manipulate the price movement of securities. Recently, the coupled hidden Markov model (CHMM)-based coupled behavior analysis (CBA) has been proposed to consider the coupling relationships in the above group-based behaviors for manipulation detection. From the modeling perspective, however, this requires overall aggregation of the behavioral data to cater for the CHMM modeling, which does not differentiate the coupling relationships presented in different forms within the aggregated behaviors and degrade the capability for further anomaly detection. Thus, this paper suggests a general CBA framework for detecting group-based market manipulation by capturing more comprehensive couplings and proposes two variant implementations, which are hybrid coupling (HC)-based and hierarchical grouping (HG)-based respectively. The proposed framework consists of three stages. The first stage, qualitative analysis, generates possible qualitative coupling relationships between behaviors with or without domain knowledge. In the second stage, quantitative representation of coupled behaviors is learned via proper methods. For the third stage, anomaly detection algorithms are proposed to cater for different application scenarios. Experimental results on data from a major Asian stock market show that the proposed framework outperforms the CHMM-based analysis in terms of detecting abnormal collaborative market manipulations. Additionally, the two different implementations are compared with their effectiveness for different application scenarios.

This article presents an efficient optimal algorithm for virtual path (VP) bandwidth allocation in telecommunication networks. Given the network topology, link capacity, routes of VP offered traffic and the condition that only one shortest VP could be chosen for each source-destination pair (single-VP), the algorithm is able to optimize the allocation of the bandwidth of VP in order to minimize the highest call blocking probability (CBP) of all the source-destination pairs. Based on the results obtained from the test-bed networks, we compare the performance of the algorithm with that of another classical optimal algorithm - "bisection algorithm" - and conclude that the former is more efficient under the condition of single-VP.

In embedded and portable applications, energy dissipation is a major design constraint. Designers must consider energy consumption. SimplePower evaluates the energy considering the system as a whole rather than just as a sum of parts, and concurrently supports both compiler and architectural experimentation. It includes a transition-sensitive, cycle-accurate datapath energy model that interfaces with analytical and transition-sensitive energy models for the memory, clock and bus subsystems, respectively. We analyzed the energy consumption of 10 codes from the multidimensional array domain, and find datapath energy hotspots, bottlenecks and helpful features. Optimized codes saved 21 percent more energy using the most recently used way-prediction cache scheme as compared to executing unoptimized codes from the multidimensional array domain.

Optimizing for energy constraints is of critical importance due to the proliferation of battery-operated embedded devices. Thus, it is important to explore both hardware and software solutions for optimizing energy. The focus of high-level compiler optimizations has traditionally been on improving performance. In this paper, we present an experimental evaluation of several state-of-the-art high-level compiler optimizations on energy consumption, considering both the processor core (datapath) and memory system. This is in contrast to many of the previous works that have considered them in isolation.

High-level compiler optimizations ha ve been widely used to ac hiev e speedups on array-based codes. Su ch optimizations are becoming increasingly important in embedded signal processing and multimedia systems. The focus of these optimizations has traditionally been on improving performance. Ho w ev er, energy constraints are of critical importance in battery-operated embedded devices. In this paper, w e presen t an experimental evaluation of several state-of-the-art compiler optimizations on energy consumption, considering both the processor core (datapath) and memory system. This is in contrast to many of the previous works that ha ve considered them in isolation.

In this paper, we presen t the design and use of a comprehensiv e framework, SimplePower, for ev aluating the effect of high-level algorithmic, architectural, and compilation trade-offs on energy. An execution-driven, cycle-accurate RT lev el energy estimation tool that uses transition sensitive energy models forms the cornerstone of this framework. SimplePower also pro vides the energy consumed in the memory system and on-chip buses using analytical energy models.

With the emergence of a plethora of embedded and portable applications, energy dissipation has joined throughput, area, and accuracy/precision as a major design constraint. Thus, designers must be concerned with both optimizing and estimating the energy consumption of circuits, architectures, and software. Most of the research in energy optimization and/or estimation has focused on single components of the system and has not looked across the interacting spectrum of the hardware and software. The novelty of our new energy estimation framework, SimplePower, is that it evaluates the energy considering the system as a whole rather than just as a sum of parts, and that it concurrently supports both compiler and architectural experimentation.