Removing Redundant Logic Pathways in Polymorphic Circuits

Evaluating the quality of software and circuit obfuscators is a research goal of great interest. However, there exists little research about evaluation of obfuscation effectiveness through analyzing and investigating redundancies found in the obfuscated variants. In this research, we consider programs represented as structural combinational circuits and then analyze obfuscated variants of those circuits through a tool that produces functionally equivalent variants based on subcircuit selection and replacement. We then consider how Boolean logic and reduction affects the size and levelization of circuit variants, giving us a concrete metric by which to consider obfuscation effectiveness. To accomplish these goals, we create an experimental environment based on a set of predefined circuits, a set of predefined algorithms which produce variants of those circuits, and a collection of logic reduction techniques and tools. We build logic reduction techniques using predefined patterns and predefined functions expressed as truth tables. As a contribution, we characterize and evaluate the effectiveness of obfuscating algorithms based on these reduction techniques. We show, for the circuits we observe, optimization on size is affected by ordering of the specific reduction patterns and functions. We also show, for the circuits we observe, reduction is affected by the specific obfuscating algorithm used to produce the variant. Based on these results, we provide a promising measurement of interest to compare both circuit variants and obfuscating algorithms

DNN Transfer Learning based Non-linear Feature Extraction for Acoustic Event Classification

Recent acoustic event classification research has focused on training suitable filters to represent acoustic events. However, due to limited availability of target event databases and linearity of conventional filters, there is still room for improving performance. By exploiting the non-linear modeling of deep neural networks (DNNs) and their ability to learn beyond pre-trained environments, this letter proposes a DNN-based feature extraction scheme for the classification of acoustic events. The effectiveness and robustness to noise of the proposed method are demonstrated using a database of indoor surveillance environments

Contextualized Generative Retrieval

The text retrieval task is mainly performed in two ways: the bi-encoder approach and the generative approach. The bi-encoder approach maps the document and query embeddings to common vector space and performs a nearest neighbor search. It stably shows high performance and efficiency across different domains but has an embedding space bottleneck as it interacts in L2 or inner product space. The generative retrieval model retrieves by generating a target sequence and overcomes the embedding space bottleneck by interacting in the parametric space. However, it fails to retrieve the information it has not seen during the training process as it depends solely on the information encoded in its own model parameters. To leverage the advantages of both approaches, we propose Contextualized Generative Retrieval model, which uses contextualized embeddings (output embeddings of a language model encoder) as vocab embeddings at the decoding step of generative retrieval. The model uses information encoded in both the non-parametric space of contextualized token embeddings and the parametric space of the generative retrieval model. Our approach of generative retrieval with contextualized vocab embeddings shows higher performance than generative retrieval with only vanilla vocab embeddings in the document retrieval task, an average of 6% higher performance in KILT (NQ, TQA) and 2X higher in NQ-320k, suggesting the benefits of using contextualized embedding in generative retrieval models

NexGen D-TCP: Next generation dynamic TCP congestion control algorithm

With the advancement of wireless access networks and mmWave New Radio (NR), new applications emerged, which requires a high data rate. The random packet loss due to mobility and channel conditions in a wireless network is not negligible, which degrades the significant performance of the Transmission Control Protocol (TCP). The TCP has been extensively deployed for congestion control in the communication network during the last two decades. Different variants are proposed to improve the performance of TCP in various scenarios, specifically in lossy and high bandwidth-delay product (high- BDP) networks. Implementing a new TCP congestion control algorithm whose performance is applicable over a broad range of network conditions is still a challenge. In this article, we introduce and analyze a Dynamic TCP (D-TCP) congestion control algorithm overmmWave NR and LTE-A networks. The proposed D-TCP algorithm copes up with the mmWave channel fluctuations by estimating the available channel bandwidth. The estimated bandwidth is used to derive the congestion control factor N. The congestion window is increased/decreased adaptively based on the calculated congestion control factor. We evaluated the performance of D-TCP in terms of congestion window growth, goodput, fairness and compared it with legacy and existing TCP algorithms. We performed simulations of mmWave NR during LOS \u3c-\u3e NLOS transitions and showed that D-TCP curtails the impact of under-utilization during mobility. The simulation results and live air experiment points out that D-TCP achieves 32:9% gain in goodput as compared to TCPReno and attains 118:9% gain in throughput as compared to TCP-Cubic

Irrigation Water Quality Standards for Indirect Wastewater Reuse in Agriculture: A Contribution toward Sustainable Wastewater Reuse in South Korea

Climate change and the subsequent change in agricultural conditions increase the vulnerability of agricultural water use. Wastewater reuse is a common practice around the globe and is considered as an alternative water resource in a changing agricultural environment. Due to rapid urbanization, indirect wastewater reuse, which is the type of agricultural wastewater reuse that is predominantly practiced, will increase, and this can cause issues of unplanned reuse. Therefore, water quality standards are needed for the safe and sustainable practice of indirect wastewater reuse in agriculture. In this study, irrigation water quality criteria for wastewater reuse were discussed, and the standards and guidelines of various countries and organizations were reviewed to suggest preliminary standards for indirect wastewater reuse in South Korea. The proposed standards adopted a probabilistic consideration of practicality and classified the use of irrigation water into two categories: upland and rice paddy. The standards suggest guidelines for E. coli, electric conductivity (EC), turbidity, suspended solids (SS), biochemical oxygen demand (BOD), pH, odor, and trace elements. Through proposing the standards, this study attempts to combine features of both the conservative and liberal approaches, which in turn could suggest a new and sustainable practice of agricultural wastewater reuse

Sound Event Detection by Pseudo-Labeling in Weakly Labeled Dataset

Weakly labeled sound event detection (WSED) is an important task as it can facilitate the data collection efforts before constructing a strongly labeled sound event dataset. Recent high performance in deep learning-based WSED’s exploited using a segmentation mask for detecting the target feature map. However, achieving accurate detection performance was limited in real streaming audio due to the following reasons. First, the convolutional neural networks (CNN) employed in the segmentation mask extraction process do not appropriately highlight the importance of feature as the feature is extracted without pooling operations, and, concurrently, a small size kernel forces the receptive field small, making it difficult to learn various patterns. Second, as feature maps are obtained in an end-to-end fashion, the WSED model would be weak to unknown contents in the wild. These limitations would lead to generating undesired feature maps, such as noise in the unseen environment. This paper addresses these issues by constructing a more efficient model by employing a gated linear unit (GLU) and dilated convolution to improve the problems of de-emphasizing importance and lack of receptive field. In addition, this paper proposes pseudo-label-based learning for classifying target contents and unknown contents by adding ’noise label’ and ’noise loss’ so that unknown contents can be separated as much as possible through the noise label. The experiment is performed by mixing DCASE 2018 task1 acoustic scene data and task2 sound event data. The experimental results show that the proposed SED model achieves the best F1 performance with 59.7% at 0 SNR, 64.5% at 10 SNR, and 65.9% at 20 SNR. These results represent an improvement of 17.7%, 16.9%, and 16.5%, respectively, over the baseline

The Impact of Impervious Surface on Water Quality and Its Threshold in Korea

The change in the impervious-pervious balance has significantly altered the stream water quality, and thus the threshold of the impervious surface area in the watershed has been an active research topic for many years. The objective of this study is to verify the correlation between impervious surfaces and water quality and to determine the threshold of the percentage of the impervious surface area (PISA) for diagnosing the severity of future stream water quality problems in the watershed as well as regulating the PISA in Korea. Statistical results indicated that the PISA is a suitable indicator of water quality at the watershed scale and can illustrate the water quality problems caused by the impervious surface. In addition, the results from this study suggest that controlling the PISA within about 10% in watersheds is a fundamental strategy to mitigate the degradation of water quality