Molecular Field-Coupled Nanocomputing (MolFCN) offers a promising post-CMOS alternative, characterized by ultra-low power consumption and high integration density. However, existing MolFCN design flows face critical challenges, including rigid clock-phase constraints, inefficient placement and routing, and the absence of accurate gate-to-device mapping. This paper introducesiFCN, an automated RTL-to-device-level design framework specifically optimized for MolFCN circuits. Building upon prior heuristic methods,iFCNincorporates inverter pruning at the RTL level to simplify circuit structure, utilizes Morton-coded linear quadtrees for efficient spatial indexing, and enhances A* routing to maximize path reuse for multi-fanout nets. To address limitations of fixed-phase clocking, we propose a hierarchical placement approach guided by Graph Convolutional Networks (GCN). The GCN learns connectivity-aware node embeddings that guide recursive partitioning and intra-layer ordering, significantly reducing wire crossings and improving placement quality. Subsequently, a lightweight adaptive method heuristically assigns clock phases to each layout layer, with careful consideration of timing and topological constraints. Additionally, we present an accurate gate-to-cell mapping algorithm to facilitate direct physical simulation and energy analyses. Benchmark results show a 30% reduction in runtime and a 10% improvement in layout area compared to our heuristic algorithm. Furthermore, the proposed method achieves comparable runtime performance to the state-of-the-artfictiontool, completing the layout of circuits with over 150 nodes in under one second. In addition, Comparative analyses against 12nm CMOS designs confirm that MolFCN circuits generated byiFCNexhibit superior area utilization and reduced power consumption, highlighting the practical potential of MolFCN for future ultra-low-power computing. All source code and data are available athttps://github.com/li-yangshuai/iFCN

Field-Coupled Nanocomputing technologies have great potential to surpass CMOS technology because of their lower power consumption and higher device concentration. To ease the burden of placement and routing (P&R) problems for FCN circuits, many delicate two-dimensional clocking schemes have been proposed, upon which algorithms can solve the P&R problems more strategically. In this paper, we propose a two-level optimization strategy by using a genetic algorithm (GA) combined with an enhanced A* algorithm. Some circuit design requirements, such as clock synchronization, layout area, etc., are cleverly designed in the fitness value function of the GA. Numerical results demonstrate the effectiveness of the hybrid algorithm. In particular, compared to current tools, such as fiction and Ropper, the proposed algorithm can achieve an optimal solution with a higher success rate and a sizeable applicable circuit scale. In addition, the concept of design rule checking (DRC) was proposed in FCN and integrated into the algorithm, making the P&R results mapping from gate-level to cell-level more smoothly. Besides, the number of cross wires is significantly reduced, and the distribution of IO ports can be more effectively controlled.

Past research has shown that over 8 months may elapse between the time when a network is compromised and the time the attack is discovered. During this long gap, attackers can steal valuable intellectual property from the victim. The recent FORGE system [8] has suggested that automatically generating fake—but believable—versions of documents can delay the attacker, cost him money, and increase his uncertainty. However, in order to generate fakes, FORGE only modifies the textual component of the document in question. But in the real world, documents consist of many non-textual components such as charts, equations, formulas, diagrams, and tables. We propose the concept of a Probabilistic Logic Graph (PLG) and show that PLGs provide a single, unified framework within which the different parts of a document can be expressed. We then define the problem of generating, for a given PLG representation of a document, a set of fake yet highly believable PLGs (i.e., documents), so that an attacker looking at them (both the original and the fake ones) cannot easily identify the original document. We show that the problem of generating fake PLGs is intractable—but we propose an approximation algorithm that solves it efficiently. We evaluate the use of PLGs over a corpus of patents and show that our fakes can effectively deceive an adversary.

Predictive Coding in legal document review, also called Text Categorization in machine learning, has been widely used in the legal industry. By leveraging machine learning technologies such as logistic regression and support vector machines (SVM), each document is assigned a probability score of its relevance to the legal case and the probabilities of documents are used to prioritize the documents to be reviewed so to improve review efficiency and cost. In recent years, deep learning technologies have been successfully applied in many text classification tasks. In predictive coding, studies were also shown better performance in some applications. Several different deep learning technologies have been used in text classifications, but there are few studies in comparisons of these technologies, especially in predictive coding. These deep learning technologies include Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), as well as RNNs with attention mechanism. This paper reports our preliminary results in comparison of different deep learning technologies in predictive coding. Specifically, the authors conducted experiments using these technologies in three open source legal document review datasets and the experimental results show that CNNs perform better than other models.

Using a novel dataset of Android banking trojans (ABTs), other Android malware, and goodware, we develop the$\sf {DBank}$system to predict whether a given Android APK is a banking trojan or not. We introduce the novel concept of aTriadic Suspicion Graph(TSG for short) which contains three kinds of nodes: goodware, banking trojans, and API packages. We develop a novel feature space based on two classes of scores derived from TSGs:suspicion scores(SUS) andsuspicion ranks(SR)—the latter yields a family of features that generalize PageRank. While TSG features (based on SUS/SR scores) provide very high predictive accuracy on their own in predicting recent (2016-2017) ABTs, we show that the combination of TSG features with previously studied lightweight static and dynamic features in the literature yields the highest accuracy in distinguishing ABTs from goodware, while preserving the same accuracy of prior feature combinations in distinguishing ABTs from other Android malware. In particular,$\sf {DBank}$’s overall accuracy in predicting whether an APK is a banking trojan or not is up to 99.9% AUC with 0.3% false positive rate. Moreover, we have already reported two unlabeled APKs from VirusTotal (which$\sf {DBank}$has detected as ABTs) to the Google Android Security Team—in one case, we discovered it before any of the 63 anti-virus products on VirusTotal did, and in the other case, we beat 62 of 63 anti-viruses on VirusTotal. This suggests that$\sf {DBank}$is capable of making new discoveries in the wild before other established vendors. We also show that our novel TSG features have some interesting defensive properties as they are robust to knowledge of the training set by an adversary: even if the adversary uses 90% of our training set and uses the exact TSG features that we use, it is difficult for him to infer$\sf {DBank}$’s predictions on APKs. We additionally identify the features that best separate and characterize ABTs from goodware as well as from other Android malware. Finally, we develop a detailed data-driven analysis of five major recent ABT families:FakeToken,Svpeng,Asacub,BankBot, andMarcher, and identify the features that best separate them from goodware and other-malware.

As the most widely used OS on earth, Android is heavily targeted by malicious hackers. Though much work has been done on detecting Android malware, hackers are becoming increasingly adept at evading ML classifiers. We develop$\textsf {FARM}$, a Feature transformation basedAndRoidMalware detector.$\textsf {FARM}$takes well-known features for Android malware detection and introduces three new types of feature transformations that transform these features irreversibly into a new feature domain. We first test$\textsf {FARM}$on 6 Android classification problems separating goodware and “other malware” from 3 classes of malware: rooting malware, spyware, and banking trojans. We show that$\textsf {FARM}$beats standard baselines when no attacks occur. Though we cannot guess all possible attacks that an adversary might use, we propose three realistic attacks on$\textsf {FARM}$and show that$\textsf {FARM}$is very robust to these attacks in all classification problems. Additionally,$\textsf {FARM}$has automatically identified two malware samples which were not previously classified as rooting malware by any of the 61 anti-viruses on VirusTotal. These samples were reported to Google’s Android Security Team who subsequently confirmed our findings.

Predictive Coding, also called Text Categorization, has been widely used in legal industry. By leveraging machine learning models such as logistic regression and SVM, the review of documents can be prioritized based on their probability of relevance to the legal case, thus improving review efficiency and cutting cost. In recent years, deep learning models-combined with word embeddings-have shown better performance in predictive coding. However, deep learning models involve many parameters and it is challenging and time-consuming for legal practitioners to select appropriate settings. Based on the experiments on several public legal text datasets, this paper shows the preliminary results about how various key parameter settings impact the performance of Convolutional Neural Networks (CNNs).

Smart rings have a unique form factor suitable for many applications, however, they offer little opportunity to provide the user with natural output. We propose passive kinesthetic force feedback as a novel output method for rotational input on smart rings. With this new output channel, friction force profiles can be designed, programmed, and felt by a user when they rotate the ring. This modality enables new interactions for ring form factors. We demonstrate the potential of this new haptic output method though Frictio, a prototype smart ring. In a controlled experiment, we determined the recognizability of six force profiles, including Hard Stop, Ramp-Up, Ramp-Down, Resistant Force, Bump, and No Force. The results showed that participants could distinguish between the force profiles with 94% accuracy. We conclude by presenting a set of novel interaction techniques that Frictio enables, and discuss insights and directions for future research.

Time domain synchronous orthogonal frequency division multiplexing (TDS-OFDM) has higher spectral efficiency than standard cyclic prefix OFDM (CP- OFDM), which is achieved by using a known pseudorandom noise (PN) sequence to replace the classical CP. However, due to the interference between the PN sequence and the data block, the performance of TDS-OFDM degrades severely over fast fading channels. To solve this problem, based on the distributed compressive sensing (DCS) theory, we propose an efficient way to realize simultaneous multi-channel reconstruction, which is achieved by using the inter-block-interference (IBI)-free region to reconstruct the high-dimensional sparse multipath channel. Specifically, we propose to utilize the temporal correlation of wireless channels as well as the channel property that path gains change much faster than path delays to simultaneously reconstruct multiple sparse channels. Then, we propose the parameterized channel estimation method based on simultaneous compressive sampling matching pursuit (S-CoSaMP) algorithm to achieve better channel estimation performance in fast time-varying channels. Simulation results demonstrate that the proposed scheme can achieve improved performance than conventional solutions.

Conventional linear precoding schemes in massive multiple-input-multiple-output (MIMO) systems, such as regularized zero-forcing (RZF) precoding, have near-optimal performance but suffer from high computational complexity due to the required matrix inversion of large size. To solve this problem, we propose a successive overrelaxation (SOR)-based precoding scheme to approximate the matrix inversion by exploiting the asymptotically orthogonal channel property in massive MIMO systems. The proposed SOR- based precoding can reduce the complexity by about one order of magnitude, and it can also approach the classical RZF precoding with negligible performance loss. We also prove that the proposed SOR-based precoding enjoys a faster convergence rate than the recently proposed Neumann-based precoding. In addition, to guarantee the performance of SOR-based precoding, we propose a simple way to choose the optimal relaxation parameter in practical massive MIMO systems. Simulation results verify the advantages of SOR-based precoding in convergence rate and computational complexity in typical massive MIMO configurations.

Massive multiple-input multiple-output (MIMO) using a large number of antennas at the base station (BS) is a promising technique for the next-generation 5G wireless communications. It has been shown that linear precoding schemes can achieve near-optimal performance in massive MIMO systems. However, classical linear precoding schemes such as zero- forcing (ZF) precoding suffer from high complexity due to the fact they require the matrix inversion of a large size. In this paper, we propose a low-complexity precoding scheme based on the least square QR (LSQR) method to realize the near-optimal performance of ZF precoding without matrix inversion. We show that the proposed LSQR-based precoding can reduce the complexity of ZF precoding by about one order of magnitude. Simulation results verify that the proposed LSQR-based precoding can provide a better tradeoff between complexity and performance than the recently proposed Neumann-based precoding.

In this paper, we propose a new method for blind modulation detection based on detailed second order cyclostationary features for underwater acoustic communication. Due to the severe and fast varying Doppler shift and phase noise in the underwater acoustic communication channel, much shorter signal length can be used to detection the modulation scheme, leading to less accuracy in the modulation detection performance. Here we propose a brand new method to perform modulation detection: by analyzing the zoomed-in spectral coherence function (SOF) of short time signal, we observe distinctive detailed features for different modulations. Taking advantage of such detailed features in short time SOF, we design a novel blind modulation detection algorithm for underwater acoustic communication system. To the best of our knowledge, our paper is the first to employ such detailed, in some sense hidden, features of cyclostationary analysis to conduct modulation detection. Coupled with our previous work on blind carrier frequency estimation and symbol rate estimation algorithms, the proposed modulation detection algorithm enjoys very high detection accuracy, high speed, and low complexity. Real experimental data collected at sea are used to validate the effectiveness of the proposed algorithm. It is also important to note that the proposed method does not assume any a priori knowledge of the target signal.

Target detection is a surveillance problem of practical importance that is well suited to wireless sensor networks. In this paper, we study the target search via track-before-detect process for moving targets in large area utilizing a network of distributed sensor nodes. Specifically, we extend the track-before-detect process to heterogeneous sensors with imperfect communication links between the sensor and fusion center. We consider two different models: (1) heterogeneous sensors with different probability of detection and perfect communication links; and (2) heterogeneous sensors with imperfect communication links. To analyze the performance of searching process for the system, we derive the expressions for the probabilities of both successful search and false search. We also investigate the strategy to optimize the system by obtaining the minimal false search when meeting the requirement of the successful search performance.

Orthogonal Frequency Division Multiplexing (OFDM) has been considered as a strong candidate for next generation high speed aerial vehicle communication systems. However, OFDM systems suffers severe performance degradation due to inter-carrier interference (ICI) in high mobility channel, if no ICI cancellation is performed. Traditionally, training symbols have been employed in one packet to help the OFDM receiver to estimate the multi-path channel and the carrier frequency offset (CFO) between the transmitter local oscillator and the receiver local oscillator. However, in aerial vehicle communication, the relative transmitter-receiver speed changes so rapidly that it is unreasonable to assume a constant speed (and CFO) during the entire packet transmission. Hence, to accurately estimate the CFO, training symbols need to be transmitted for every OFDM symbol. Obviously, this significantly reduces OFDM throughput while adding complexity due to repeated CFO estimation. In this paper, we extend our previous work to propose a joint channel/CFO estimation and ICI cancellation algorithm. Specifically, in our previous work, we have proposed a total ICI cancellation algorithm using parallel processing for OFDM system which offers the excellent ICI cancellation and BER performance. However, in this work, perfect channel information was assumed. In this paper, we combine the channel estimation with the ICI cancellation together. The proposed general total ICI cancellation algorithm has the ability to jointly estimate the carrier frequency offset and channel information, and improve the performance significantly. Meanwhile, a serial processing is proposed to reduce the computation complexity. Simulation results in different scenarios confirm the performance of the proposed scheme in multipath fading channels for high speed aerial vehicle communication.

In recently developed cognitive radio network (CRN), the spectrum sharing leads to many uncertainties associated with the aggregate interference in the network. It is highly desired to build an interference model for such cognitive radio networks to express such uncertainties to quantify the effect of the interference on the primary network. However, existing interference models have not account for lognormal shadowing due to the difficulty to estimate the entire lognormal sum distribution. In this paper, we propose to utilize the Scaled Student's t distribution to approximate the shadowing effect and improve existing interference models in CRN. Closed form probability density function (PDF), cumulative distribution function (CDF) and characteristic function (CF) of the interference including shadowing effects are derived. Simulation results of CDF, complementary CDF (CCDF), CF and bit error rate (BER) performance in various scenarios confirm the effectiveness of the proposed approximation method.

In this paper, we implement and demonstrate an adaptive interference avoidance TDCS (Transform-domain Communication System) based cognitive radio via software defined radio implementation. By dynamically notching the occupied bands prior to applying IDFT, the designed cognitive radio communicates without interference to primary users and from primary users, as well as provides coexistence between primary users and secondary users. The designed system applies GNU software radio as the platform, and USRP (Universal Software Radio Peripheral) as the hardware solution. This cognitive radio is capable of detecting primary users in real time and adaptively adjusting its transmission parameters to avoid interference to primary users. Additionally, we have demonstrated that when the primary user transmission changes, the cognitive radio dynamically adjusts its transmission accordingly. We have demonstrated seamless real time video transmission between two cognitive radio nodes, while avoiding interference from primary users and interference to primary users operating in the same spectrum.