Miracle-dz/UniFAR-FTUs huggingface dataset:
Distributed Abnormal Behavior Detection Approach Based on Deep Belief Network and Ensemble SVM Using Spark

The emergence of Internet connectivity has led to a significant increase in the volume and complexity of cyber attacks. Abnormal behavior detection systems are valuable tools for ensuring the security in computer networks. However, due to the huge amount and ever increasing diversity of the intrusions, the existing intrusion detection systems, which use machine learning techniques to learn a classifier based on a handcrafted feature vector, are not robust enough to detect sophisticated attacks which cause a high false alarm rate. Therefore, building a flexible in-depth defense system to detect abnormal behavior requires an ability to automatically learn powerful features and analyze large amounts of network traffic. To address these concerns, this paper proposes a novel distributed approach for the detection of abnormal behavior in large-scale networks. The developed model discovers the abnormal behavior from large-scale network traffic data using a combination of a deep feature extraction and multi-layer ensemble support vector machines (SVMs) in a distributed way. First, we perform a non-linear dimensionality reduction, achieved through a distributed deep belief networks on large-scale network traffic data. Then, the obtained features are fed to the multi-layer ensemble SVM. The construction of the ensemble is accomplished through the iterative reduce paradigm based on Spark. Empirical results show a promising gain in performance compared with other existing models.
Promoting Binary Hash Code's Discriminability by Category Centers for Image Retrieval

In the field of fast and large-scale image retrieval, the binary hash code based on convolutional features enjoys powerful advantages. CNN model's leading recognition ability shows that its high-level features distribute intensively and have stable between-class distances. On the basis of the distribution characteristic of convolutional features, this paper presents a new method of improving retrieval performance by means of using the category centers of high-level features to enhance the common semantic content of high-level features of the query image and making the consequent binary hash code gain better retrieval results. Experimental results indicate that (i) the category centers of CNN feature offer comparable discriminability with the initial model when identifying the semantic information of images; (ii) before calculating binary hash code, the integration of the CNN feature of a query image with category centers weighted by predicted probabilities can boost performance in similar image retrieval. State-of-the-art retrieval performance is obtained on the Cifar-10 dataset in this paper among all hash methods.
Design of a Biomimetic Robot Fish for Realization of Coefficient of Drag with Control Architecture and Fuzzy Logic Algorithm for Autonomous Obstacle Avoidance

Underwater exploration is recently heading towards emphasizing the research for unknown, complex deep-sea environments where huge mineral resources along with immense living flora and fauna are abundant. Biomimetic robot fish plays an important role in overcoming the drawbacks of a conventional AUV confronting issues such as propulsion efficiency, mechanical robustness, maneuverability, adaptability and autonomy in oceanographic environments. A new design and development methodology is formulated for the culmination of robot fish. A control architecture and algorithm for autonomous obstacle avoidance is simulated in a virtual environment using MATLAB platform. The mechanical design model is certified for safety swimming by conducting static analysis, dynamic analysis along with the coefficient of drag of fish robot realization and with various bio-inspired locomotion patterns of fish designed for self-propelling feature with higher anatomy. On a concluding note, this paper is discerning about a new approach to design a fish robot with the intact autonomous featured vehicle.
The application of Linear Predictive Coding for RoIP data transmission in the HFC network

Recently, HFC (hybrid fiber cable) has been widely used due to the demand for economical and efficient digital services. HFC can be used for an integrated digital service broadband network access technology, through the photoelectric conversion process to achieve data transmission between different devices and equipment. Therefore, RoIP (Radio Over IP) technology for two-way communication is revealed without changing the original system hardware structure. RoIP is a technology that converts radio frequency signal generated at a transmitting end into digital signal and transmits it using an optical IP network. However, during the signal conversion process, a large amount of data generated needs to be compressed to achieve efficient data transmission. In this paper, we reduce the amount of data by combining linear predictive coding techniques, which is widely used in speech signal processing, with nonlinear quantization coding. By using this method, RF signals can be transmitted efficiently. We measured the compression ratio and the magnitude of the error vector representing the degree of signal damage during the compression process and compared it with previous experimental results to verify the availability of these schemes.
Beam-Aware Dormant and Scheduling Mechanism for 5G Millimeter Wave Cellular Systems

In this paper, an analytical model for millimeter wave (mmWave) cellular systems that characterizes user equipment (UE) power-saving constraint is studied. On the basis of the realistic radio propagation, we model the channel quality by curve fitting and create a Markov chain based channel model. The reference signal for channel measurement in conventional cellular systems can be transmitted to the whole cell at a time, but the beam sweeping is needed in mmWave systems in order to overcome the high path loss of high-frequency electromagnetic wave. This constraint further affects the timing of channel measurement, channel quality feedback, and power consumption. A novel beam-aware dormant mechanism for power saving in mmWave systems is proposed. UEs may enter the dormant mode for power-saving when the base station (BS) is not transmitting on their beams. Therefore, the BS is suggested to consider the time gap between channel measurement and data transmission when scheduling UEs. Beam-aware scheduling framework is proposed and integrated with the beam-aware dormant mechanism. It is shown that the policy to schedule a UE after its channel measurement outperforms the one to schedule every time slot. The simulation results and mathematical expression both demonstrate that the beam-aware media access control (MAC) framework improves throughput under power constraint.
Colorectal Segmentation Using Multiple Encoder-Decoder Network in Colonoscopy Images

Colorectal cancer is the third most common cancer which causes of cancer-related deaths. Therefore, early diagnosis of polyps by colonoscopy could result in successful treatment. Diagnosis of polyps in colonoscopy videos is a challenging task due to variations in the size and shape of polyps. In this paper, we propose a polyp segmentation method based on the encoder-decoder network. Performance of the method is enhanced by two strategies, we perform a novel database augmentation method for colonoscopy images in the training phase. Besides, in the test phase, we perform an effective prediction by combining multi-model to compare the probability of each image that is produced by the network. Evaluation of the proposed method using the ETIS-LariPolypDB database shows that our proposed method outperforms state-of-the-art results.
Quo Vadis Network Anonymity?

The Internet promises an ever-increasing variety of services available anytime, almost anywhere, to anyone of just about any experience level. Thus, in many respects, the virtual world has become a viable alternative to our real world. With all of the convenience and freely-available information that this virtual world provides, it has one major problem: in the real-world people can nearly always exert some control over their privacy. However, on the Internet, users have few controls, if any, over the privacy of their actions. The only path towards enabling true network privacy is to provide anonymity. Anonymity services can allow users to carry out their activities anonymously on the Internet. In this work we investigate the following technical question: how should the anonymity technology be integrated into the Internet?
Lessons Learned from Analyzing Dynamic Promotion for User-Level Threading

A performance vs. practicality trade-off exists between user-level threading techniques. The community has settled mostly on a black-and-white perspective; fully fledged threads assume that suspension is imminent and incur overheads when suspension does not take place, and run-to-completion threads are more lightweight but less practical since they cannot suspend. Gray areas exist, however, whereby threads can start with minimal capabilities and then can be dynamically promoted to acquire additional capabilities when needed. This paper investigates the full spectrum of threading techniques from a performance vs. practicality trade-off perspective on modern multicore and many-core systems. Our results indicate that achieving the best trade-off highly depends on the suspension likelihood; dynamic promotion is more appropriate when suspension is unlikely and represents a solid replacement for run to completion, thanks to its lower programming constraints, while fully fledged threads remain the technique of choice when suspension likelihood is high.
Improving Performances of Log Mining for Anomaly Prediction Through NLP-Based Log Parsing

Failure prediction of industrial systems is a promising application domain for data mining approaches and should naturally rely on log messages which are a prime source of data as they are generated by many systems. However, before extracting relevant information of such log messages, another critical step is to parse the logs, that is to say to transform a raw unstructured text from the log messages into a suitable input for data mining. These two problems (log parsing then log mining) are often studied separately while they are directly related in the context of failure prediction; moreover, few performance benchmarks are publicly available. In this paper, we focus on the impact of log parsing techniques via natural language processing on the performances of log mining on two datasets. The first one is a log of an industrial aeronautical system comprising over 4,500,000 messages collected over one year of operation; the second one is a public benchmark set from an HDFS cluster. On the latter, we show that it is possible to raise the F-score from 96% to 99.2% while using simpler and more robust log parsing techniques that require less parameter tuning provided that they are correctly combined with log mining techniques.
Expanding Queries for Code Search Using Semantically Related API Class-names

When encountering unfamiliar programming tasks (e.g., connecting to a database), there is a need to seek potential working code examples. Instead of using code search engines, software developers usually post related programming questions on online Q&A forums (e.g., Stack Overflow). One possible reason is that existing code search engines would return effective code examples only if a query contains identifiers (e.g., class or method names). In other words, existing code search engines do not handle natural-language queries well (e.g., a description of a programming task). However, developers may not know the appropriate identifiers at the time of the search. As the demand of searching code examples is increasing, it is of significant interest to enhance code search engines. We conjecture that expanding natural-language queries with their semantically related identifiers has a great potential to enhance code search engines. In this paper, we propose an automated approach to find identifiers (in particular API class-names) that are semantically related to a given natural-language query. We evaluate the effectiveness of our approach using 74 queries on a corpus of 23,677,216 code snippets that are extracted from 24,666 open source Java projects. The results show that our approach can effectively recommend semantically related API class-names to expand the original natural-language queries. For instance, our approach successfully retrieves relevant code examples in the top 10 retrieved results for 76 percent of 74 queries, while it is 36 percent when using the original natural-language query; and the median rank of the first relevant code example is increased from 22 to 7.
The Physical Activity Guidelines for Americans

Importance Approximately 80% of US adults and adolescents are insufficiently active. Physical activity fosters normal growth and development and can make people feel, function, and sleep better and reduce risk of many chronic diseases. Objective To summarize key guidelines in the Physical Activity Guidelines for Americans, 2nd edition (PAG). Process and Evidence Synthesis The 2018 Physical Activity Guidelines Advisory Committee conducted a systematic review of the science supporting physical activity and health. The committee addressed 38 questions and 104 subquestions and graded the evidence based on consistency and quality of the research. Evidence graded as strong or moderate was the basis of the key guidelines. The Department of Health and Human Services (HHS) based the PAG on the 2018 Physical Activity Guidelines Advisory Committee Scientific Report. Recommendations The PAG provides information and guidance on the types and amounts of physical activity to improve a variety of health outcomes for multiple population groups. Preschool-aged children (3 through 5 years) should be physically active throughout the day to enhance growth and development. Children and adolescents aged 6 through 17 years should do 60 minutes or more of moderate-to-vigorous physical activity daily. Adults should do at least 150 minutes to 300 minutes a week of moderate-intensity, or 75 minutes to 150 minutes a week of vigorous-intensity aerobic physical activity, or an equivalent combination of moderate- and vigorous-intensity aerobic activity. They should also do muscle-strengthening activities on 2 or more days a week. Older adults should do multicomponent physical activity that includes balance training as well as aerobic and muscle-strengthening activities. Pregnant and postpartum women should do at least 150 minutes of moderate-intensity aerobic activity a week. Adults with chronic conditions or disabilities, who are able, should follow the key guidelines for adults and do both aerobic and muscle-strengthening activities. Recommendations emphasize that moving more and sitting less will benefit nearly everyone. Individuals performing the least physical activity benefit most by even modest increases in moderate-to-vigorous physical activity. Additional benefits occur with more physical activity. Both aerobic and muscle-strengthening physical activity are beneficial. Conclusions and Relevance The Physical Activity Guidelines for Americans, 2nd edition, provides information and guidance on the types and amounts of physical activity that provide substantial health benefits. Health professionals and policy makers should facilitate awareness of the guidelines and promote the health benefits of physical activity and support efforts to implement programs, practices, and policies to facilitate increased physical activity and to improve the health of the US population.
Dynamically Negotiating Capacity Between On-demand and Batch Clusters

In the era of rapid experimental expansion data analysis needs are rapidly outpacing the capabilities of small institutional clusters and looking to integrate HPC resources into their workflow. We propose one way of reconciling on-demand needs of experimental analytics with the batch managed HPC resources within a system that dynamically moves nodes between an on-demand cluster configured with cloud technology (OpenStack) and a traditional HPC cluster managed by a batch scheduler (Torque). We evaluate this system experimentally both in the context of real-life traces representing two years of a specific institutional need, and via experiments in the context of synthetic traces that capture generalized characteristics of potential batch and on-demand workloads. Our results for the real-life scenario show that our approach could reduce the current investment in on-demand infrastructure by 82% while at the same time improving the mean batch wait time almost by an order of magnitude (8x).
Effectiveness of Integrated Neurocognitive Therapy on Cognitive Impairment and Functional Outcome for Schizophrenia Outpatients

Cognitive impairment is highly prevalent in patients with schizophrenia and schizoaffective disorder. Many interventions have been developed to treat cognitive deficit, since it has a strong impact on functional outcome; however, there are no integrated interventions targeting multiple neuro- and social-cognitive domains with a particular focus on the generalization of the effects of therapy on the functional outcome. Recently, a group of experts has developed a cognitive remediation group therapy approach called Integrated Neurocognitive Therapy (INT), which includes exercises to improve the MATRICS (Measurement and Treatment Research to Improve Cognition in Schizophrenia) neuro- and social-cognitive domains. This systematic review and meta-analysis aimed to assess the efficacy of this approach. We conducted a search of PubMed, Scopus, Web of Science, and PsycINFO to select primary studies evaluating INT in schizophrenic and schizoaffective patients. The primary outcomes of the meta-analysis included negative and positive symptoms and global functioning. Two randomized controlled trials met inclusion criteria. A total of 217 participants were included. Based on the results from the Positive and Negative Syndrome Scale (PANSS), a significant pooled effect size was observed for negative symptoms, which demonstrated not only an improvement in the patients treated immediately after therapy but also a permanence of positive results at a 9–12-month follow-up. On the other hand, no significant effect size was observed for positive symptoms. In addition, a significant pooled effect size was found for Global Assessment of Functioning (GAF), which shows how INT's integrated approach has lasting positive implications on patients' functional outcome. We concluded that INT might be an effective treatment for negative symptoms and global functioning in patients with schizophrenia, compared to treatment as usual (TAU).
The Management of Partial Zone II Intrasynovial Flexor Tendon Lacerations: A Literature Review of Biomechanics, Clinical Outcomes, and Complications.

241e unbiased fashion. It is noteworthy that our study1 was entirely independent of any company’s input and that we achieved a 100 percent satisfaction rate with the postsurgical outcome among both patients and surgeons despite suffering from a learning curve. Although the tale of Goldilocks is typically interpreted today as a discovery of what is “just right,” it was understood differently in the past. It was viewed as a cautionary tale that imparts a lesson about the hazards of wandering off and exploring unknown territory.6 However, doing just that is of pivotal importance for us plastic surgeons. Innovation will be the key to our ongoing success and survival as a specialty. Unlike neurosurgery or cardiology, plastic surgery does not “own” any part of the human body, and our continued growth depends on the development of new solutions to unmet medical needs.7 Therefore, we should never be afraid of trying out new technologies, techniques, and devices. However, it is our responsibility to critically and independently evaluate any innovation no matter how exciting. DOI: 10.1097/PRS.0000000000005147
A Novel Radio Resource Optimization Method for Relay-Based Unmanned Aerial Vehicles

Unmanned aircraft systems (UASs) have attracted worldwide attention in recent years because of their potential applicability in a multitude of fields such as environmental monitoring and the communication provisioning environment. These applications enable data to be remotely collected from distant places through unmanned aerial vehicles (UAVs) equipped with communication devices. However, the communication range of a single UAV is limited because of the restrictions of the wireless transceiver as a result of the payload limitation. Therefore, UASs consisting of relay-based UAVs in which data are transferred via multiple UAVs have been studied to achieve long-distance communication with a high throughput. The time division long-term evolution advanced (TD-LTE-A) relaying technique may be applicable to UASs as it offers a reasonable solution. However, this technique is currently ineffective for UASs due to the limitation caused by its frame configuration. To alleviate this issue, in this paper, we propose a method to optimize radio resources based on the TD-LTE-A frame structure, which is capable of realizing efficient relay communication. Our proposed method considers a flexible frame configuration and updates cycle of control information to overcome the shortcoming of the conventional scheme. The effectiveness of the proposed radio resource optimization is demonstrated through simulation under varying environments.
Development of Low-Cost Autonomous Surface Vehicles (ASV) for Watershed Quality Monitoring

Water has always been an important part of human life, in the context of the global environment, water management and conservation is the focus because it impacts on human survival in a fundamental way. Current condition of the Kalibaru River Basin: (a) water use for agriculture, plantation and community needs is increasingly increasing, (b) unstable availability of water, (c) excessive utilization that does not pay attention to carrying capacity, (d) potentially for erosion and (e) high sources of water pollution that affect the quality of raw materials of drinking water, ecosystem, economy and human health and social security. This study aims to develop a system consisting of hardware and software capable of monitoring the quality of watersheds, in this research used Kalibaru's Watershed. This monitoring can be used by relevant agencies to conduct studies or even make policies related to quality watershed care.
Fast Network Simulation Through Approximation or: How Blind Men Can Describe Elephants

Network researchers today are unable to test their new ideas at scale before deployment due to the prohibitive costs of custom testbeds and the slow speed of large-scale network simulators. Data center simulation is particularly slow because of the massive amount of bandwidth and high degree of redundant computation incurred in simulating the network stacks of thousands of commodity machines. By using approximation to replace redundant portions of the simulation, we improve computation time while retaining high accuracy.