Securing Smart Grid In-Network Aggregation through False Data Detection

Existing prevention-based secure in-network data aggregation schemes for the smart grids cannot e ectively detect accidental errors and falsified data injected by malfunctioning or compromised meters. In this work, we develop a light-weight anomaly detector based on kernel density estimator to locate the smart meter from which the falsified data is injected. To reduce the overhead at the collector, we design a dynamic grouping scheme, which divides meters into multiple interconnected groups and distributes the verification and detection load among the root of the groups. To enable outlier detection at the root of the groups, we also design a novel data re-encryption scheme based on bilinear mapping so that data previously encrypted using the aggregation key is transformed in a form that can be recovered by the outlier detectors using a temporary re-encryption key. Therefore, our proposed detection scheme is compatible with existing in-network aggregation approaches based on additive homomorphic encryption. We analyze the security and eÿciency of our scheme in terms of storage, computation and communication overhead, and evaluate the performance of our outlier detector with experiments using real-world smart meter consumption data. The results show that the performance of the light-weight detector yield high precision and recall

Application of numerical simulation on eliminating shrinkage defect of automobile wheel hub

Shrinkage defect is a serious problem encountered during the development of automobile wheel hub made from ductile cast iron. In order to find out the reason for shrinkage formation and eliminate it in time, numerical simulation technology was performed to analyze the casting solidification process, and two modified casting projects were brought forward. Based on the simulation result, the solidification characteristics of the original project were compared with two modified projects and accordingly the optimized casting project with chill and rider feeding was selected for application during the development of the wheel hub. The result shows that casting shrinkage defect has been effectively controlled and the trial production cycle of the wheel hub was significantly shortened

Check Me If You Can: Detecting ChatGPT-Generated Academic Writing using CheckGPT

With ChatGPT under the spotlight, utilizing large language models (LLMs) for academic writing has drawn a significant amount of discussions and concerns in the community. While substantial research efforts have been stimulated for detecting LLM-Generated Content (LLM-content), most of the attempts are still in the early stage of exploration. In this paper, we present a holistic investigation of detecting LLM-generate academic writing, by providing a dataset, evidence, and algorithms, in order to inspire more community effort to address the concern of LLM academic misuse. We first present GPABenchmark, a benchmarking dataset of 600,000 samples of human-written, GPT-written, GPT-completed, and GPT-polished abstracts of research papers in CS, physics, and humanities and social sciences (HSS). We show that existing open-source and commercial GPT detectors provide unsatisfactory performance on GPABenchmark, especially for GPT-polished text. Moreover, through a user study of 150+ participants, we show that it is highly challenging for human users, including experienced faculty members and researchers, to identify GPT-generated abstracts. We then present CheckGPT, a novel LLM-content detector consisting of a general representation module and an attentive-BiLSTM classification module, which is accurate, transferable, and interpretable. Experimental results show that CheckGPT achieves an average classification accuracy of 98% to 99% for the task-specific discipline-specific detectors and the unified detectors. CheckGPT is also highly transferable that, without tuning, it achieves ~90% accuracy in new domains, such as news articles, while a model tuned with approximately 2,000 samples in the target domain achieves ~98% accuracy. Finally, we demonstrate the explainability insights obtained from CheckGPT to reveal the key behaviors of how LLM generates texts

Detecting Review Spam: Challenges and Opportunities

Abstract-Online customer reviews for both products or merchants have greatly affected others' decision making in purchase. Considering the easily accessibility of the reviews and the significant impacts to the retailers, there is an increasing incentive to manipulate the reviews, mostly profit driven. Without proper protection, spam reviews will cause gradual loss of credibility of the reviews and corrupt the entire online review systems eventually. Therefore, review spam detection is considered as the first step towards securing the online review systems. In this paper, we aim to overview existing detection approaches in a systematic way, define key research issues, and articulate future research challenges and opportunities for review spam detection. Index Terms-Review spam, review spammer, spam behav ior

New threats to health data privacy

<p>Abstract</p> <p>Background</p> <p>Along with the rapid digitalization of health data (e.g. Electronic Health Records), there is an increasing concern on maintaining data privacy while garnering the benefits, especially when the data are required to be published for secondary use. Most of the current research on protecting health data privacy is centered around data de-identification and data anonymization, which removes the identifiable information from the published health data to prevent an adversary from reasoning about the privacy of the patients. However, published health data is not the only source that the adversaries can count on: with a large amount of information that people voluntarily share on the Web, sophisticated attacks that join disparate information pieces from multiple sources against health data privacy become practical. Limited efforts have been devoted to studying these attacks yet.</p> <p>Results</p> <p>We study how patient privacy could be compromised with the help of today’s information technologies. In particular, we show that private healthcare information could be collected by aggregating and associating disparate pieces of information from multiple online data sources including online social networks, public records and search engine results. We demonstrate a real-world case study to show user identity and privacy are highly vulnerable to the attribution, inference and aggregation attacks. We also show that people are highly identifiable to adversaries even with inaccurate information pieces about the target, with real data analysis.</p> <p>Conclusion</p> <p>We claim that too much information has been made available electronic and available online that people are very vulnerable without effective privacy protection.</p

Intracranial electrophysiological recordings on a swine model of mesial temporal lobe epilepsy

ObjectiveTo test the feasibility and reliability of intracranial electrophysiological recordings in an acute status epilepticus model on laboratory swine.MethodIntrahippocampal injection of kainic acid (KA) was performed on 17 male Bama pigs (Sus scrofa domestica) weighing between 25 and 35 kg. Two stereoelectroencephalography (SEEG) electrodes with a total of 16 channels were implanted bilaterally along the sensorimotor cortex to the hippocampus. Brain electrical activity was recorded 2 h daily for 9–28 days. Three KA dosages were tested to evaluate the quantities capable of evoking status epilepticus. Local field potentials (LFPs) were recorded and compared before and after the KA injection. We quantified the epileptic patterns, including the interictal spikes, seizures, and high-frequency oscillations (HFOs), up to 4 weeks after the KA injection. Test–retest reliability using intraclass correlation coefficients (ICCs) were performed on interictal HFO rates to evaluate the recording stability of this model.ResultsThe KA dosage test suggested that a 10 μl (1.0 μg/μl) intrahippocampal injection could successfully evoke status epilepticus lasting from 4 to 12 h. At this dosage, eight pigs (50% of total) had prolonged epileptic events (tonic-chronic seizures + interictal spikes n = 5, interictal spikes alone n = 3) in the later 4 weeks of the video-SEEG recording period. Four pigs (25% of total) had no epileptic activities, and another four (25%) had lost the cap or did not complete the experiments. Animals that showed epileptiform events were grouped as E + (n = 8) and the four animals showing no signs of epileptic events were grouped as E– (n = 4). A total of 46 electrophysiological seizures were captured in the 4-week post-KA period from 4 E + animals, with the earliest onset on day 9. The seizure durations ranged from 12 to 45 s. A significant increase of hippocampal HFOs rate (num/min) was observed in the E+ group during the post-KA period (weeks 1, 2,4, p &lt; 0.05) compared to the baseline. But the E-showed no change or a decrease (in week 2, p = 0.43) compared to their baseline rate. The between-group comparison showed much higher HFO rates in E + vs. E – (F = 35, p &lt; 0.01). The high ICC value [ICC (1, k) = 0.81, p &lt; 0.05] quantified from the HFO rate suggested that this model had a stable measurement of HFOs during the four-week post-KA periods.SignificanceThis study measured intracranial electrophysiological activity in a swine model of KA-induced mesial temporal lobe epilepsy (mTLE). Using the clinical SEEG electrode, we distinguished abnormal EEG patterns in the swine brain. The high test–retest reliability of HFO rates in the post-KA period suggests the utility of this model for studying mechanisms of epileptogenesis. The use of swine may provide satisfactory translational value for clinical epilepsy research

Practical and Secure Outsourcing Algorithms of Matrix Operations Based on a Novel Matrix Encryption Method

With the recent growth and commercialization of cloud computing, outsourcing computation has become one of the most important cloud services, which allows the resource-constrained clients to efficiently perform large-scale computation in a pay-per-use manner. Meanwhile, outsourcing large scale computing problems and computationally intensive applications to the cloud has become prevalent in the science and engineering computing community. As important fundamental operations, large-scale matrix multiplication computation (MMC), matrix inversion computation (MIC), and matrix determinant computation (MDC) have been frequently used. In this paper, we present three new algorithms to enable secure, verifiable, and efficient outsourcing of MMC, MIC, and MDC operations to a cloud that may be potentially malicious. The main idea behind our algorithms is a novel matrix encryption/decryption method utilizing consecutive and sparse unimodular matrix transformations. Compared to previous works, this versatile technique can be applied to many matrix operations while achieving a good balance between security and efficiency. First, the proposed algorithms provide robust confidentiality by concealing the local information of the entries in the input matrices. Besides, they also protect the statistic information of the original matrix. Moreover, these algorithms are highly efficient. Our theoretical analysis indicates that the proposed algorithms reduce the time overhead on the client side from O(n 2.3728639 ) to O(n 2 ). Finally, the extensive experimental evaluations demonstrate the practical efficiency and effectiveness of our algorithms

Inharmonious Region Localization by Magnifying Domain Discrepancy

Inharmonious region localization aims to localize the region in a synthetic image which is incompatible with surrounding background. The inharmony issue is mainly attributed to the color and illumination inconsistency produced by image editing techniques. In this work, we tend to transform the input image to another color space to magnify the domain discrepancy between inharmonious region and background, so that the model can identify the inharmonious region more easily. To this end, we present a novel framework consisting of a color mapping module and an inharmonious region localization network, in which the former is equipped with a novel domain discrepancy magnification loss and the latter could be an arbitrary localization network. Extensive experiments on image harmonization dataset show the superiority of our designed framework. Our code is available at https://github.com/bcmi/MadisNet-Inharmonious-Region-Localization

Chinese Medicines for Preventing and Treating Radiation-Induced Pulmonary Injury: Still a Long Way to Go

Thoracic radiotherapy is a mainstay of the treatment for lung, esophageal, and breast cancers. Radiation-induced pulmonary injury (RIPI) is a common side effect of thoracic radiotherapy, which may limit the radiotherapy dose and compromise the treatment results. However, the current strategies for RIPI are not satisfactory and may induce other side effects. Chinese medicines (CMs) have been used for more than a thousand years to treat a wide range of diseases, including lung disorders. In this review, we screened the literature from 2007 to 2017 in different online databases, including China National Knowledge Infrastructure (CNKI), Chongqing VIP, Wanfang, and PubMed; summarized the effectiveness of CMs in preventing and treating RIPI; explored the most frequently used drugs; and aimed to provide insights into potential CMs for RIPI. Altogether, CMs attenuated the risk of RIPI with an occurrence rate of 11.37% vs. 27.78% (P < 0.001) compared with the control groups. We also found that CMs (alone and combined with Western medical treatment) for treating RIPI exerted a higher efficacy rate than that of the control groups (78.33% vs. 28.09%, P < 0.001). In the screened literature, 38 CMs were used for the prevention and treatment of RIPI. The top five most frequently used CMs were Astragali Radix (with a frequency of 8.47%), Ophiopogonis Radix (with a frequency of 6.78%), Glycyrrhizae Radix et Rhizome (with a frequency of 5.08%), Paeoniae Radix Rubra (with a frequency of 5.08%), and Prunellae Spica (with a frequency of 5.08%). However, further high-quality investigations in CM source, pharmacological effects and underlying mechanisms, toxicological aspects, and ethical issues are warranted. Taken together, CMs might have a potential role in RIPI prevention and treatment and still have a long way to investigate

Emodin Induced SREBP1-Dependent and SREBP1-Independent Apoptosis in Hepatocellular Carcinoma Cells

Reynoutria multiflora (Thunb.) Moldenke (He Shou Wu) has been used for about 20 centuries as a Chinese medicinal herb for its activities of anticancer, anti-hyperlipidemia, and anti-aging. Previously, we found that He Shou Wu ethanol extract could induce apoptosis in hepatocellular carcinoma cells, and we also screened its active components. In this study, we investigated whether lowering lipid metabolism of emodin, a main active component in He Shou Wu, was associated with inhibitory effects in hepatocellular carcinoma cells. The correlation of apoptosis induction and lipid metabolism was investigated. The intrinsic apoptotic cell death, lipid production, and their signaling pathways were investigated in emodin-treated human hepatocellular carcinoma cells Bel-7402. The data showed that emodin triggered apoptosis in Bel-7402 cells. The mitochondrial membrane potential (ΔΨm) was reduced in emodin-treated Bel-7402 cells. We also found that emodin activated the expression of intrinsic apoptosis signaling pathway-related proteins, cleaved-caspase 9 and 3, Apaf 1, cytochrome c (CYTC), apoptosis-inducing factor, endonuclease G, Bax, and Bcl-2. Furthermore, the level of triglycerides and desaturation of fatty acids was reduced in Bel-7402 cells when exposed to emodin. Furthermore, the expression level of messenger RNA (mRNA) and protein of sterol regulatory element binding protein 1 (SREBP1) as well as its downstream signaling pathway and the synthesis and the desaturation of fatty acid metabolism-associated proteins (adenosine triphosphate citrate lyase, acetyl-CoA carboxylase alpha, fatty acid synthase (FASN), and stearoyl-CoA desaturase D) were also decreased. Notably, knock-out of SREBP1 in Bel-7402 cells was also found to induce less intrinsic apoptosis than did emodin. In conclusion, these results indicated that emodin could induce apoptosis in an SREBP1-dependent and SREBP1-independent manner in hepatocellular carcinoma cells