Miracle-dz/UniFAR-FTUs huggingface dataset:
Stream-based Machine Learning for Network Security and Anomaly Detection

Data Stream Machine Learning is rapidly gaining popularity within the network monitoring community as the big data produced by network devices and end-user terminals goes beyond the memory constraints of standard monitoring equipment. Critical network monitoring applications such as the detection of anomalies, network attacks and intrusions, require fast and continuous mechanisms for on-line analysis of data streams. In this paper we consider a stream-based machine learning approach for network security and anomaly detection, applying and evaluating multiple machine learning algorithms in the analysis of continuously evolving network data streams. The continuous evolution of the data stream analysis algorithms coming from the data stream mining domain, as well as the multiple evaluation approaches conceived for benchmarking such kind of algorithms makes it difficult to choose the appropriate machine learning model. Results of the different approaches may significantly differ and it is crucial to determine which approach reflects the algorithm performance the best. We therefore compare and analyze the results from the most recent evaluation approaches for sequential data on commonly used batch-based machine learning algorithms and their corresponding stream-based extensions, for the specific problem of on-line network security and anomaly detection. Similar to our previous findings when dealing with off-line machine learning approaches for network security and anomaly detection, our results suggest that adaptive random forests and stochastic gradient descent models are able to keep up with important concept drifts in the underlying network data streams, by keeping high accuracy with continuous re-training at concept drift detection times.
AP-Attack: A Novel User Re-identification Attack On Mobility Datasets

Since the advent of hand held devices (e.g., smartphones, tablets, smart watches) with Ubiquitous computing and the wide popularity of location-based mobile applications, the amount of captured user location data is dramatically increasing. However, the gathering and exploitation of this data by mobile application providers raises many privacy threats as sensitive information can be inferred from it (e.g., home and work locations, religious beliefs, sexual orientations and social relationships). To address this issue a number of data obfuscation techniques (also called Location Privacy Protection Mechanisms or LPPMs) have been proposed in the literature. One of the existing methods to assess the effectiveness of LPPMs is to test them against user re-identification attacks. The aim of these attacks is to break user anonymity by re-associating data obfuscated using a given LPPM with user profiles built from user past mobility. In this paper, we present AP-Attack a novel re-identification attack that relies on a heatmap representation of user mobility data. Our experiments run against three representative LPPMs of the literature using four real mobility datasets show that AP-Attack succeeds in re-identifying up to 79% users in non-obfuscated data, +27% more users than POI-Attack and PIT-Attack two well known state-of-the-art attacks. We also present a simple technique to improve user protection against our attack, which relies on a user-centric application of multiple-LPPMs.
LISA: Explaining Recurrent Neural Network Judgments via Layer-wIse Semantic Accumulation and Example to Pattern Transformation

Recurrent neural networks (RNNs) are temporal networks and cumulative in nature that have shown promising results in various natural language processing tasks. Despite their success, it still remains a challenge to understand their hidden behavior. In this work, we analyze and interpret the cumulative nature of RNN via a proposed technique named as Layer-wIse-Semantic-Accumulation (LISA) for explaining decisions and detecting the most likely (i.e., saliency) patterns that the network relies on while decision making. We demonstrate (1) LISA: “How an RNN accumulates or builds semantics during its sequential processing for a given text example and expected response” (2) Example2pattern: “How the saliency patterns look like for each category in the data according to the network in decision making”. We analyse the sensitiveness of RNNs about different inputs to check the increase or decrease in prediction scores and further extract the saliency patterns learned by the network. We employ two relation classification datasets: SemEval 10 Task 8 and TAC KBP Slot Filling to explain RNN predictions via the LISA and example2pattern.
Dissociating neural signatures of mental state retrodiction and classification based on facial expressions

Abstract Posed facial expressions of actors have often been used as stimuli to induce mental state inferences, in order to investigate ‘Theory of Mind’ processes. However, such stimuli make it difficult to determine whether perceivers are using a basic or more elaborated mentalizing strategy. The current study used as stimuli covert recordings of target individuals who viewed various emotional expressions, which caused them to spontaneously mimic these expressions. Perceivers subsequently judged these subtle emotional expressions of the targets: in one condition (‘classification’) participants were instructed to classify the target's expression (i.e. match it to a sample) and in another condition (‘retrodicting’) participants were instructed to retrodict (i.e. infer which emotional expression the target was viewing). When instructed to classify, participants showed more prevalent activations in event‐related brain potentials (ERPs) at earlier and mid‐latency ERP components N170, P200 and P300‐600. By contrast, when instructed to retrodict participants showed enhanced late frontal and fronto‐temporal ERPs (N800‐1000), with more sustained activity over the right than the left hemisphere. These findings reveal different cortical processes involved when retrodicting about a facial expression compared to merely classifying it, despite comparable performance on the behavioral task.
Correlation of Betel Quid with Oral Cancer from 1998 to 2017: A Study Based on Bibliometric Analysis

Background: Betel quid chewing has been a major risk factor for oral cancer (OC) in southern China. This study aimed to analyze the scientific publications on the relationship between betel quid chewing and OC and construct a model to quantitatively and qualitatively evaluate pertinent publications from 1998 to 2017. Methods: The publications from 1998 to 2017 were retrieved from the Web of Science Core Collection database. Microsoft Excel, Thomson Data Analyzer, VOSviewer, and CiteSpace software were used to analyze the publication outcomes, journals, countries/regions, institutions, authors, research areas, and research frontiers. Results: A total of 788 publications on the relationship between betel quid chewing and OC published until October 25, 2017, were identified. The top 4 related journals were Journal of Oral Pathology Medicine, Oral Oncology, Plos One, and International Journal of Cancer. The top five countries engaged in related research included China, India, the United States, the United Kingdom, and Malaysia. The corresponding disciplines, such as oncology, oral surgery, pathology, environmental and occupational health, and toxicology, were mainly concentrated in three disciplines. The subject terms squamous cell carcinoma, OC, betel quid, expression, oral submucous fibrosis, India, and p53 ranked first among research hotspots. The burst terms squamous cell carcinoma, OC, betel quid, and expression ranked first in research frontiers. Conclusions: Research in this area emphasized hotspots such as squamous cell carcinoma, OC, oral submucosal fibrosis, betel quid, and tobacco. The annual number of publications steadily decreased from 1998 to 2017, with a lack of a systematic study from interdisciplinary perspectives, inadequate pertinent journals, limited regions with the practice of betel quid chewing, and insufficient participation of researchers, which indicate that as the prevalence of OC increases, particularly in China, research in this area warrants further expansion.
Effect of dietary fiber levels on bacterial composition with age in the cecum of meat rabbits

This study investigated the influence of dietary fiber levels on the growth performance, digestion, metabolism, and cecal microbial community of rabbits with different diets at different age. The different levels of dietary natural detergent fiber (NDF) were formulated accordingly: 400(A), 350(B), 300(C), 250(D) g/kg original matter basis, respectively; the different ages were 52, 62, and 72 days. With NDF increasing, the average daily feed intake (ADFI) and feed conversion rate (FCR) were increased, whereas average daily gain (ADG) and mortality were decreased (p < 0.05). The stomach relative weight, stomach content relative weight, cecal relative weight, and cecal content weight increased with increasing NDF (p < 0.05). The NH3‐N concentration of cecum dropped when the dietary NDF increased (p < 0.05). The diversity of the total microbiota increased significantly in Diets B, C (p = 0.011), and reached the lowest in 52 days for all diet groups. The richness index was decreased significantly in Diet A, D (p < 0.05) and in 62 days (p < 0.001), respectively. The phylum Firmicutes was higher (p < 0.01) in rabbits fed Diets B, C than Diets A, D and Bacteroidetes was highest in Diets C, D, and Proteobacteria was the highest in Diet A (p < 0.001). Among the classified genera, there were 14 that had levels of abundance of more than 1% and were commonly shared by all samples. Ruminococcus spp. that produced volatile fatty acid (VFA) abundance was highest from Diets B, C at 52 and 62 days. It is interesting to note that Bifidobacterium from Diet C was the most abundant genus during the entire experimental period (p < 0.01). The data from Venn diagrams, principal component analysis (PCA), and heat map plots of the bacterial communities showed that there were more groups of shared microbiota with aging. The above results indicate the cecal microbiota controlled by the 350 g/kg NDF diet can prevent gastrointestinal distress and exhibit good production performance.
Cascade proportional integral retarded control of servodrives

The goal of this work is to propose a new time-delay control law called the cascade proportional integral retarded controller, which is aimed at position control of DC servodrives. The proposed controller has a cascade inner loop-outer loop structure. The inner loop is endowed with an integral retarded algorithm, and regulates the servodrive angular velocity. A proportional controller closes the outer loop whose goal is to regulate the servodrive angular position. The tuning of the cascade proportional integral retarded controller is accomplished into two steps. In the first step, the velocity of the inner loop is tuned by assigning a triple dominant root. The second step tunes the outer position loop. It is also possible to modify the cascade proportional integral retarded controller to avoid velocity measurements without adding extra filters. Moreover, the cascade topology of the cascade proportional integral retarded controller makes it easy to introduce a nonlinear saturated gain in the outer loop. This controller termed as the cascade nonlinear proportional integral retarded controller prevents overshoots for large values of the set point, avoids excessive control effort, and maintains a prescribed value of the angular velocity. Experiments in real-time using a laboratory prototype allow assessing the performance of the proposed controllers.
Circuit Switched VM Networks for Zero-Copy IO

Although applications are nowadays often executed in virtual machines (VMs) to isolate applications or consolidate physical machines, VM network performance is still challenging. Packetization, encapsulation, congestion control, preparations for loss, and copying of data introduce unnecessary performance degradation within a system where VMs communicate over abundant and reliable shared-memory. Although protocols like TCP are therefore not well suited for kernel network stack in VMs, preexisting applications require the kernel socket interface to keep functioning. In eliminating the unnecessary overhead for inter-VM communication and shifting it to the host operating system for communication over a physical NIC, our approach increases performance for both cases of communicating with another VM on the same host and for communicating with external hosts. Instead of multiplexing multiple connections over a single virtual Ethernet link, we use a separate shared-memory connection for each VM application socket. Our approach improves the stream and datagram performance of existing applications over an unmodified socket interface and brings the benefits of memory-mapped zero-copy IO to modified applications without sacrificing isolation between sockets.
Stock Price Correlation Coefficient Prediction with ARIMA-LSTM Hybrid Model

Predicting the price correlation of two assets for future time periods is important in portfolio optimization. We apply LSTM recurrent neural networks (RNN) in predicting the stock price correlation coefficient of two individual stocks. RNNs are competent in understanding temporal dependencies. The use of LSTM cells further enhances its long term predictive properties. To encompass both linearity and nonlinearity in the model, we adopt the ARIMA model as well. The ARIMA model filters linear tendencies in the data and passes on the residual value to the LSTM model. The ARIMA LSTM hybrid model is tested against other traditional predictive financial models such as the full historical model, constant correlation model, single index model and the multi group model. In our empirical study, the predictive ability of the ARIMA-LSTM model turned out superior to all other financial models by a significant scale. Our work implies that it is worth considering the ARIMA LSTM model to forecast correlation coefficient for portfolio optimization.
Clinical evaluation of a substitute of HLA-B*58:01 in different Chinese ethnic groups

Abstract The goal of this research was to investigate the linkage disequilibrium between rs9263726 and HLA-B*58:01 in different Chinese ethnic groups (Han, Tibet, and Hui) and to study the feasibility of rs9263726 replacing HLA-B*58:01 as an efficient indicator of potential allopurinol hypersensitivity syndrome. In this study, rs9263726 and HLA-B*58:01 were detected in all samples. For samples of individuals whose rs9263726 genotypes were not consistent with HLA-B*58:01, we did high-resolution typing of HLA-B gene to further confirm the correlation of rs9263726 genotype and special HLA-B alleles. We confirmed that the linkage disequilibrium between rs9263726 and HLA-B*58:01 was more significant in the Han ethnic group (r2=0.886, D’=1.0) than in the Tibet and Hui ethnic groups (for Tibetan, r2=0.606, D’=0.866; for Hui, r2=0.622, D’=0.924). For Han Chinese, samples with the GG genotype of rs9263726 did not carry HLA-B*58:01, while AA genotype samples were homozygous carriers of HLA-B*58:01. However, GA genotype samples of rs9263726 required a more sophisticated HLA-B genotyping assay before it was possible to identify whether they were HLA-B*58:01 carriers or not. For Tibetan and Hui, the linkage disequilibrium between rs9263726 and HLA-B*58:01 was not significant. Therefore, rs9263726 cannot replace HLA-B*58:01 in these two groups.
Therapeutic Effect of Intestinal Autochthonous Lactobacillus reuteri P16 Against Waterborne Lead Toxicity in Cyprinus carpio

Harmful effects of heavy metals are myriad. Lead (Pb) from soil and atmosphere contaminates water bodies and affects the aquatic animals. Our previous study confirmed the in vitro probiotic potential of Lactobacillus reuteri against Pb toxicity, but further investigation is necessary for gaining insights into the related protection mode. Therefore, in this study, we investigated the protective effects of the potential probiotic L. reuteri P16 against waterborne Pb exposure-induced toxicity in the freshwater fish Cyprinus carpio. Fish (average weight: 23.16 ± 0.73 g) were allocated to four groups (control, Pb only, Pb + L. reuteri P16, and L. reuteri P16 only) and Pb groups were exposed to waterborne Pb (1 mg L−1) for 6 weeks. L. reuteri P16 (108 CFU g−1) supplemented diet was provided twice daily. Growth performances, hemato-biochemical parameters, innate immune responses, intestinal microbiota, and Pb accumulation in tissues were measured at the end of the trial. When the fish were exposed to Pb, dietary supplementation of L. reuteri P16 effectively decreased mortality and accumulation of Pb in tissues, and improved the growth performance. Co-treatment with Pb and L. reuteri P16 alleviated Pb exposure-induced oxidative stress, reversed alterations in hemato-biochemical parameters, improved innate immune parameters, and restored intestinal enzymatic activities. Moreover, L. reuteri P16 supplementation reversed the changes in intestinal microbiota in Pb-exposed fish. Furthermore, Pb exposure decreased the expressions of pro-inflammatory cytokines (TNF-α, IL-1β). However, the expression of heat shock proteins (HSP70 and HSP90) increased, which might have increased the cellular stress. Interestingly, the Pb-induced alterations of gene expressions were reversed by L. reuteri P16 supplementation. Thus, dietary administration of the potential probiotic L. reuteri P16 had several beneficial effects on growth performance and immune responses, decreased Pb accumulation in tissues, and reversed alterations in hematological responses of C. carpio. Furthermore, it offered direct protection against Pb-induced oxidative stress. Therefore, L. reuteri P16 may be a novel dietary supplement for enhancing growth performance and preventing Pb-exposure-induced toxicity in fish in aquaculture and aquatic products.
Sustained Effects of Memory and Lifestyle Interventions on Memory Functioning of Older Adults: An 18-Month Follow-Up Study

Background: There has been much research devoted to examining the short-term effects of different interventions for improving memory functioning of older adults with memory complaints. Nevertheless, very few studies have examined the long-term effects of these interventions. Thus, the present study compared the sustained effects of a conventional memory intervention (MI) and a Chinese lifestyle intervention on improving memory functioning in older adults. Methods: Twenty-nine older adults who were aged 60 years and older and had memory complaints were recruited. Each completed 10 weekly sessions of the Dejian Mind-body Intervention (DMBI; n = 11) or MI (n = 18) approximately 18 months ago. Participants’ verbal and visual memory functioning and their subjective impression of the changes of their memory performance and physical and psychological health status were evaluated. Results: Results showed significant improvements in memory in both intervention groups at the follow-up assessments when compared with baseline. In addition, older adults in both intervention groups perceived improved memory performance and physical and psychological wellness at follow-up, with the DMBI group reporting significantly greater improvements in physical health compared to the MI group. Conclusion: Altogether, the present study provides supportive evidence that the DMBI and MI might be two effective remedies for older adults to improve or preserve their memory functioning with relatively sustained effects.
Am I Responsible for End-User's Security? A Programmer's Perspective

Previous research has pointed that software applications should not depend on programmers to provide security for end-users as majority of programmers are not experts of computer security. On the other hand, some studies have revealed that security experts believe programmers have a major role to play in ensuring the end-users' security. However, there has been no investigation on what programmers perceive about their responsibility for the end-users' security of applications they develop. In this work, by conducting a qualitative experimental study with 40 software developers, we attempted to understand the programmer's perception on who is responsible for ensuring end-users' security of the applications they develop. Results revealed majority of programmers perceive that they are responsible for the end-users' security of applications they develop. Furthermore, results showed that even though programmers aware of things they need to do to ensure end-users' security, they do not often follow them. We believe these results would change the current view on the role that different stakeholders of the software development process (i.e. researchers, security experts, programmers and Application Programming Interface (API) developers) have to play in order to ensure the security of software applications.
Scaling Notifications Beyond Alerts: From Subtly Drawing Attention up to Forcing the User to Take Action

Research has been done in sophisticated notifications, still, devices today mainly stick to a binary level of information, while they are either attention drawing or silent. We propose scalable notifications, which adjust the intensity level reaching from subtle to obtrusive and even going beyond that level while forcing the user to take action. To illustrate the technical feasibility and validity of this concept, we developed three prototypes. The prototypes provided mechano-pressure, thermal, and electrical feedback, which were evaluated in different lab studies. Our first prototype provides subtle poking through to high and frequent pressure on the user's spine, which significantly improves back posture. In a second scenario, the user is able to perceive the overuse of a drill by an increased temperature on the palm of a hand until the heat is intolerable, forcing the user to eventually put down the tool. The last application comprises of a speed control in a driving simulation, while electric muscle stimulation on the users' legs, conveys information on changing the car's speed by a perceived tingling until the system forces the foot to move involuntarily. In conclusion, all studies' findings support the feasibility of our concept of a scalable notification system, including the system forcing an intervention.
Mining Spatial-temporal Correlation of Sensory Data for Estimating Traffic Volumes on Highways

Sensory data are often of low quality, for example, data are incomplete, ambiguous, or indirect, which has become the bottleneck of many data-driven applications. Two kinds of data which are handled in the paper for estimating traffic volumes on highways are no exception. In particular, the traffic volume data obtained from the loop detectors are accurate but sparse, and the mobile signaling data for estimating relative traffic volumes are wide in coverage and low in cost, but they are indirect and inaccurate. Keeping the characteristics of data in mind, the paper proposes a data fusion approach named Polaris which extends compressive sensing to estimate traffic volumes on highways. The Polaris analyzes the sparsity of the traffic volumes reported by detectors, mines the spatial-temporal correlations between the two kinds of data, and then gives the computational steps in the light of compressive sensing. Experiments are conducted on the large-scale real signaling data and the loop detector data. The experimental results show that the Polaris has the lowest estimation errors in comparison with several other methods. The corresponding Polaris system has been built and deployed in Fujian Province, China. It can obtain real-time traffic volumes on the highways with full coverage at a very low cost.1