Duo: Software Defined Intrusion Tolerant System Using Dual Cluster

An intrusion tolerant system (ITS) is a network security system that is composed of redundant virtual servers that are online only in a short time window, called exposure time. The servers are periodically recovered to their clean state, and any infected servers are refreshed again, so attackers have insufficient time to succeed in breaking into the servers. However, there is a conflicting interest in determining exposure time, short for security and long for performance. In other words, the short exposure time can increase security but requires more servers to run in order to process requests in a timely manner. In this paper, we propose Duo, an ITS incorporated in SDN, which can reduce exposure time without consuming computing resources. In Duo, there are two types of servers: some servers with long exposure time (White server) and others with short exposure time (Gray server). Then, Duo classifies traffic into benign and suspicious with the help of SDN/NFV technology that also allows dynamically forwarding the classified traffic to White and Gray servers, respectively, based on the classification result. By reducing exposure time of a set of servers, Duo can decrease exposure time on average. We have implemented the prototype of Duo and evaluated its performance in a realistic environment

Continually Updating Generative Retrieval on Dynamic Corpora

Generative retrieval has recently been gaining a lot of attention from the research community for its simplicity, high performance, and the ability to fully leverage the power of deep autoregressive models. However, prior work on generative retrieval has mostly investigated on static benchmarks, while realistic retrieval applications often involve dynamic environments where knowledge is temporal and accumulated over time. In this paper, we introduce a new benchmark called STREAMINGIR, dedicated to quantifying the generalizability of retrieval methods to dynamically changing corpora derived from StreamingQA, that simulates realistic retrieval use cases. On this benchmark, we conduct an in-depth comparative evaluation of bi-encoder and generative retrieval in terms of performance as well as efficiency under varying degree of supervision. Our results suggest that generative retrieval shows (1) detrimental performance when only supervised data is used for fine-tuning, (2) superior performance over bi-encoders when only unsupervised data is available, and (3) lower performance to bi-encoders when both unsupervised and supervised data is used due to catastrophic forgetting; nevertheless, we show that parameter-efficient measures can effectively mitigate the issue and result in competitive performance and efficiency with respect to the bi-encoder baseline. Our results open up a new potential for generative retrieval in practical dynamic environments. Our work will be open-sourced.Comment: Work in progres

An unusual cause of duodenal perforation due to a lollipop stick

Children have a natural tendency to explore objects with their mouths; this can result in the swallowing of foreign objects. Most ingested foreign bodies pass uneventfully through the gastrointestinal tract. However, some foreign bodies cause obstruction or perforation of the gastrointestinal tract, requiring surgical intervention. Perforation of the gastrointestinal tract may be associated with considerable morbidity and mortality. The most common sites of intestinal foreign body perforation are the ileocecal and rectosigmoid regions. Foreign body perforation of the duodenum is relatively uncommon. We report the first Korean case of duodenal perforation by an ingested 8-cm lollipop stick. Lollipops are popular with the children and fairly accessible to them, as most parents are not aware of their potential harm. Pediatric clinicians should be aware of the risks associated with lollipop stick ingestion. Our report also describes the feasibility and safety of laparoscopic diagnosis and management of pediatric patients with peritonitis induced by the ingestion of foreign bodies

Refining Historical earthquake Data Through Modeling and Scale Model Tests

This study was performed for the reevaluation of historical earthquake records which occurred in Korea through tests and numerical analyses. For the scale model tests, static and cyclic lateral load tests on wooden frames that constitute a Korean ancient commoner’s house were conducted. Full-scale models of two types of frames were used for testing. Two 1:4 scale models were tested for rock and soil foundation conditions. Scaled real earthquake time histories were inputted for the tests. The peak ground acceleration (PGA) at the collapse of the house at the soil site was 0.25g, whereas PGA for moderate damage at the rock site was 0.6g. The intensity of major historical earthquake records related with house collapses was reevaluated based on the results of these scale mode1 tests. The magnitudes of historical earthquake records related with house collapses were estimated considering the magnitude, epicentral distance, soil condition and aging of the house. Eighteen artificial time histories for magnitudes 6-8, epicentral distances 5 km - 350 km and hard and soft soil condition were generated. The aging effects of the house was modeled as the lateral loading capacity of wooden frames represented by hysteretic stiffness decreased linearly with time

Experimental Investigation for Tensile Performance of GFRP-Steel Hybridized Rebar

Tensile performance of the recently developed “FRP Hybrid Bar” at Korea Institute of Civil Engineering and Building Technology (KICT) is experimentally evaluated by the authors. FRP Hybrid Bar is introduced to overcome the low elastic modulus of the existing GFRP bars to be used as a structural member in reinforced concrete structures. The concept of material hybridization is applied to increase elastic modulus of GFRP bars by using steel. This hybridized GFRP bar can be used in concrete structures as a flexural reinforcement with a sufficient level of elastic modulus. In order to verify the effect of material hybridization on tensile properties, tensile tests are conducted. The test results for both FRP Hybrid Bar and the existing GFRP bars are compared. The results indicate that the elastic modulus of FRP Hybrid Bar can be enhanced by up to approximately 250 percent by the material hybridization with a sufficient tensile strength. To ensure the long-term durability of FRP Hybrid Bar to corrosion resistance, the individual and combined effects of environmental conditions on FRP Hybrid Bar itself as well as on the interface between rebar and concrete are currently under investigation

High‐Color‐Purity Subtractive Color Filters with a Wide Viewing Angle Based on Plasmonic Perfect Absorbers

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/110824/1/adom201400533.pd

Knowledge Unlearning for Mitigating Privacy Risks in Language Models

Pretrained Language Models (LMs) memorize a vast amount of knowledge during initial pretraining, including information that may violate the privacy of personal lives and identities. Previous work addressing privacy issues for language models has mostly focused on data preprocessing and differential privacy methods, both requiring re-training the underlying LM. We propose knowledge unlearning as an alternative method to reduce privacy risks for LMs post hoc. We show that simply applying the unlikelihood training objective to target token sequences is effective at forgetting them with little to no degradation of general language modeling performances; it sometimes even substantially improves the underlying LM with just a few iterations. We also find that sequential unlearning is better than trying to unlearn all the data at once and that unlearning is highly dependent on which kind of data (domain) is forgotten. By showing comparisons with a previous data preprocessing method known to mitigate privacy risks for LMs, we show that unlearning can give a stronger empirical privacy guarantee in scenarios where the data vulnerable to extraction attacks are known a priori while being orders of magnitude more computationally efficient. We release the code and dataset needed to replicate our results at https://github.com/joeljang/knowledge-unlearning

Forage Production and Nutritive Value of Four Promising Sorghum X Sudangrass Hybrid in Korea

Sorghum x sudangrass hybrid (Sorghum bicolor (L.) Moench) is one of the most important annual grass utilized for supplemental summer forage. There were 14 National Livestock Cooperative Federation (NLCF)’s recommended forage sorghum´sudangrass hybrids in Korea. Among them several hybrids were produced widely, however, they have not been evaluated under same environmental conditions. Therefore, a field experiment was carried out to compare the plant height, forage yield and nutritive value of the NLCF’s recommended cultivars at NLRI, Suwon, Korea in 1995. The four promising cultivars of sorghum´sudangrass hybrid used in this study were P 988, TE-Haygrazer, NC+ 855 (heading type), and Jumbo (headless type). The plant height ranged between 220cm (P 988) and 232cm (NC+ 855). NC+ 855 was classified as early-maturing cultivar, and then TE-Haygrazer, P 988. Jumbo was going on vegetative stage in this experiment. The dry matter (DM) yields of sorghum´sudangrass hybrids ranged between 11.26 and 13.40 MT/ha. No significant differences were observed, but the DM yield of NC+ 855 was slightly higher than those of P 988 (11.31MT), TE-Haygrazer (11.77MT), and Jumbo (11.26MT). The nutritive value was very similar among the cultivars. There were no significant differences in contents of crude protein, crude fiber, and crude protein yield. In conclusion, there were no differences of forage yield and nutritive value among four promising recommended sorghum´sudangrass hybrids, although maturity was different among heading-type hybrids