Miracle-dz/UniFAR-FTUs huggingface dataset:
Feature Selection for Machine Learning-Based Early Detection of Distributed Cyber Attacks

It is well known that distributed cyber attacks simultaneously launched from many hosts have caused the most serious problems in recent years including problems of privacy leakage and denial of services. Thus, how to detect those attacks at early stage has become an important and urgent topic in the cyber security community. For this purpose, recognizing C&C (Command & Control) communication between compromised bots and the C&C server becomes a crucially important issue, because C&C communication is in the preparation phase of distributed attacks. Although attack detection based on signature has been practically applied since long ago, it is well-known that it cannot efficiently deal with new kinds of attacks. In recent years, ML(Machine learning)-based detection methods have been studied widely. In those methods, feature selection is obviously very important to the detection performance. We once utilized up to 55 features to pick out C&C traffic in order to accomplish early detection of DDoS attacks. In this work, we try to answer the question that "Are all of those features really necessary? " We mainly investigate how the detection performance moves as the features are removed from those having lowest importance and we try to make it clear that what features should be payed attention for early detection of distributed attacks. We use honeypot data collected during the period from 2008 to 2013. SVM(Support Vector Machine) and PCA(Principal Component Analysis) are utilized for feature selection and SVM and RF(Random Forest) are for building the classifier. We find that the detection performance is generally getting better if more features are utilized. However, after the number of features has reached around 40, the detection performance will not change much even more features are used. It is also verified that, in some specific cases, more features do not always means a better detection performance. We also discuss 10 important features which have the biggest influence on classification.
Using BiLSTM in Dependency Parsing for Vietnamese

Recently, deep learning methods have achieved good results in dependency parsing for many natural languages. In this paper, we investigate the use of bidirectional long short-term memory network models for both transition-based and graph-based dependency parsing for the Vietnamese language. We also reportour contribution in building a Vietnamese dependency treebank whose tagset conforms to the Universal Dependency schema. Various experiments demonstrate the efficiency of this method, which achieves the best parsing accuracy in comparison to other existing approaches on the same corpus, with unlabeled attachment score of 84.45% or labeled attachment scoreof 78.56%.
Wrist Kinematics and Kinetics during Wheelchair Propulsion with a Novel Handle-based Propulsion Mechanism

Wheelchairs are important for daily mobility and recreation of the elderly and physically challenged individuals. The non-ergonomical propulsion design of wheelchairs frequently causes injuries to the upper extremities, especially in the wrist region. In this study, the joint kinematics and kinetics of the wrist joint was analyzed on subjects propelling a novel handle based wheelchair propulsion unit at 1.2m/s speed (20W and 40W respectively). The joint kinematics and the joint moments of the wrist were determined using a 3D musculoskeletal model. The mean values of joint ranges and torques during propulsion at the mentioned speeds were lower when compared to standard wheelchair propulsion with the push-rim, making the novel propulsion mechanism with its compact design an interesting alternative for wheelchair users that might help to avoid or reduce joint injuries.
Comparing Manual and Machine Annotations of Emotions in Non-acted Speech

Psychological well-being at the workplace has increased the demand for detecting emotions with higher accuracies. Speech, one of the most non-obtrusive modes of capturing emotions at the workplace, is still in need of robust emotion annotation mechanisms for non-acted speech corpora. In this paper, we extend our experiments on our non-acted speech database in two ways. First, we report how participants themselves perceive the emotion in their voice after a long gap of about six months, and how a third person, who has not heard the clips earlier, perceives the emotion in the same utterances. Both annotators also rated the intensity of the emotion. They agreed better in neutral (84%) and negative clips (74%) than in positive ones (38%). Second, we restrict our attention to those samples that had agreement and show that the classification accuracy of 80% by machine learning, an improvement of 7% over the state-of-the-art results for speakerdependent classification. This result suggests that the high-level perception of emotion does translate to the low-level features of speech. Further analysis shows that the silently expressed positive and negative emotions are often misinterpreted as neutral. For the speaker-independent test set, we report an overall accuracy of 61%.
Executive function deficit in betel-quid-dependent chewers: Mediating role of prefrontal cortical thickness

Background: Betel quid is the fourth most popular psychoactive agent worldwide. Neuroimaging studies have suggested betel-quid dependence is accompanied by abnormality in brain structure and function. However, the neural correlates of executive function deficit and prefrontal cortical thickness associated with betel-quid chewing still remain unclear. Objective: The present study aimed to examine the relationship between executive function deficit and prefrontal cortical thickness in chronic betel-quid chewers. Methods: Twenty-three betel-quid-dependent chewers and 26 healthy controls were recruited to participate in this study. Executive function was tested using three tasks. Cortical thickness analysis was analyzed with the FreeSurfer software package. Results: Behavioral results suggested a profound deficit of executive function in betel-quid-dependent chewers. Cortical thickness analysis revealed thinner cortex in the bilateral dorsolateral prefrontal cortex in betel-quid-dependent chewers. Further analysis suggested that cortical thickness of the bilateral dorsolateral prefrontal cortex mediated the correlation of betel-quid chewing and executive function. Conclusions: These results suggest the important role of executive function and cortical thickness of the dorsolateral prefrontal cortex with betel-quid chewing. Our findings provide evidence that executive function deficit may be mediated by the cortical thickness of the dorsolateral prefrontal cortex. These results could potentially help us develop novel ways to diagnose and prevent betel-quid dependence.
Molecular characterization of the giant freshwater prawn (Macrobrachium rosenbergii) beta-actin gene promoter

Constitutive promoters are important tools for gene function studies and transgenesis. The Beta-actin (actb1) gene promoter has been isolated from many species but remains to be cloned from the giant freshwater prawn (Macrobrachium rosenbergii). In this study, we cloned and characterized the Mractb1 gene promoter. Two alternative promoters were identified for the Mractb1 gene, which direct the generation of two transcripts with different 5′ untranslated regions. Three CpG islands were predicted in the upstream sequence, which are intimately related to transcription initiation and promoter activity. In addition to the CCAAT-box and the CArG-box, molecular dissection of the flanking sequence revealed the existence of one negative and two positive elements in the upstream region and the first intron. Finally, the Mractb1 promoter demonstrated comparative activity to the carp (Cyprinus carpio) actb1 promoter. Our investigations provide a valuable genetic tool for gene function studies and shed light on the regulation of the Mractb1 gene.
Textual Relationship Modeling for Cross-Modal Information Retrieval

Feature representation of different modalities is the main focus of current cross-modal information retrieval research. Existing models typically project texts and images into the same embedding space. In this paper, we explore the multitudinous of textural relationships in text modeling. Specifically, texts are represented by a graph generated using various textural relationships including semantic relations, statistical co-occurrence, and predefined knowledge base. A joint neural model is proposed to learn feature representation individually in each modality. We use Graph Convolutional Network (GCN) to capture relation-aware representations of texts and Convolutional Neural Network (CNN) to learn image representations. Comprehensive experiments are conducted on two benchmark datasets. The results show that our model outperforms the state-of-the-art models significantly by 6.3% on the CMPlaces data and 3.4% on English Wikipedia, respectively.
Heuristic Evaluation: Comparing Generic and Specific Usability Heuristics for Identification of Usability Problems in a Living Museum Mobile Guide App

This paper reports on an empirical study that compares two sets of heuristics, Nielsen’s heuristics and the SMART heuristics in the identification of usability problems in a mobile guide smartphone app for a living museum. Five experts used the severity rating scales to identify and determine the severity of the usability issues based on the two sets of usability heuristics. The study found that Nielsen’s heuristics set is too general to detect usability problems in a mobile application compared to SMART heuristics which focuses on the smartphone application in the product development lifecycle instead of the generic Nielsen’s heuristics which focuses on a wide range of interactive system. The study highlights the importance of utilizing domain specific usability heuristics in the evaluation process. This ensures that relevant usability issues were successfully identified which could then be given immediate attention to ensure optimal user experience.
Evaluation of antioxidant status and oxidative stress markers in follicular fluid for human in vitro fertilization outcome

Antioxidant status and oxidative stress markers in human follicular fluid (FF) surrounding oocytes may be related to the outcomes of in vitro fertilization and embryo transfer (IVF‐ET). Therefore, we herein examined the relationship between antioxidant status and oxidative stress markers in FF and the outcomes of IVF‐ET.
Microfluidic device embedding electrodes for dielectrophoretic manipulation of cells‐A review

Microfluidic device embedding electrodes realizes cell manipulation with the help of dielectrophoresis. Cell manipulation is an important technology for cell sorting and cell population purification. Till now, the theory of dielectrophoresis has been greatly developed. Microfluidic devices with various arrangements of electrodes have been reported from the beginning of the single non‐uniform electric field to the later multiple physical fields. This paper reviews the research status of microfluidic device embedding electrodes for cell manipulation based on dielectrophoresis. Firstly, the working principle of dielectrophoresis is explained. Next, cell manipulation approaches based on dielectrophoresis are introduced. Then, different types of electrode arrangements in the microfluidic device for cell manipulation are discussed, including planar, multilayered and microarray dot electrodes. Finally, the future development trend of the dielectrophoresis with the help of microfluidic devices is prospected. With the rapid development of microfluidic technology, in the near future, high precision, high throughput, high efficiency, multifunctional, portable, economical and practical microfluidic dielectrophoresis will be widely used in the fields of biology, medicine, agriculture and so on.
Recent researches on innovative drone technologies in robotics field

ABSTRACT Drones have achieved great success in research and development and in many industrial fields in the last five years, and there are enormous expectations for their further utilization. It was predicted that the market size of drone technologies will rapidly expand in the near future and is expected to reach $12 billion by 2021. In such circumstances, drones have become one of the main research topics in the robotics field, and the number of studies has increased. However, there is little research in the literature that has classified the many studies related to drones and introduced research trends. This paper aims to properly classify drone studies in the robotics field and to summarize the research trends for each classification. GRAPHICAL ABSTRACT
Network Brain-Computer Interface (nBCI): An Alternative Approach for Cognitive Prosthetics

Brain computer interfaces (BCIs) have been applied to sensorimotor systems for many years. However, BCI technology has broad potential beyond sensorimotor systems. The emerging field of cognitive prosthetics, for example, promises to improve learning and memory for patients with cognitive impairment. Unfortunately, our understanding of the neural mechanisms underlying these cognitive processes remains limited in part due to the extensive individual variability in neural coding and circuit function. As a consequence, the development of methods to ascertain optimal control signals for cognitive decoding and restoration remains an active area of inquiry. To advance the field, robust tools are required to quantify time-varying and task-dependent brain states predictive of cognitive performance. Here, we suggest that network science is a natural language in which to formulate and apply such tools. In support of our argument, we offer a simple demonstration of the feasibility of a network approach to BCI control signals, which we refer to as network BCI (nBCI). Finally, in a single subject example, we show that nBCI can reliably predict online cognitive performance and is superior to certain common spectral approaches currently used in BCIs. Our review of the literature and preliminary findings support the notion that nBCI could provide a powerful approach for future applications in cognitive prosthetics.
Specialized Positioning of Myonuclei Near Cell-Cell Junctions

Skeletal muscles are large cells with multiple nuclei that are precisely positioned. The importance of the correct nuclear position is highlighted by the correlation between mispositioned nuclei and muscle disease (Spiro et al., 1966; Gueneau et al., 2009). Myonuclei are generally considered to be equivalent and therefore how far nuclei are from their nearest neighbor is the primary measurement of nuclear positioning. However, skeletal muscles have two specialized cell-cell contacts, the neuromuscular (NMJ) and the myotendinous junction (MTJ). Using these cell-cell contacts as reference points, we have determined that there are at least two distinct populations of myonuclei whose position is uniquely regulated. The post-synaptic myonuclei (PSMs) near the NMJ, and the myonuclei near the myotendinous junction myonuclei (MJMs) have different spacing requirements compared to other myonuclei. The correct positioning of pairs of PSMs depends on the specific action of dynein and kinesin. Positions of the PSMs and MJMs relative to the junctions that define them depend on the KASH-domain protein, Klar. We also found that MJMs are positioned close to the MTJ as a consequence of muscle stretching. Our study defines for the first time that nuclei in skeletal muscles are not all equally positioned, and that subsets of distinct myonuclei have specialized rules that dictate their spacing.
A New PC-Based Text Entry System Based on EOG Coding

Some disadvantages of optical eye tracking systems have increased the interest to EOG (Electrooculography) based Human Computer Interaction (HCI). However, text entry attempts using EOG have been slower than expected because the eyes should move several times for entering a character. In order to improve the writing speed and accuracy of EOG based text entry, a new method based on the coding of eye movements has been suggested in this study. In addition, a real time EOG based HCI system has developed to implement the method. In our method all characters have been encoded by single saccades in 8 directions and different dwell time. In order to standardize dwell times and facilitate the coding process, computer assisted voice guidance was used. A number of experiments have been conducted to examine the effectiveness of the proposed method and system. At the end of the fifth trials, an experienced user was able to write at average 13.2 wpm (5 letters = 1 word) with 100% accuracy using the developed system. The results of our experiments have shown that text entry with the eye can be done quickly and efficiently with the proposed method and system.
Truth Will Out: Departure-Based Process-Level Detection of Stealthy Attacks on Control Systems

Recent incidents have shown that Industrial Control Systems (ICS) are becoming increasingly susceptible to sophisticated and targeted attacks initiated by adversaries with high motivation, domain knowledge, and resources. Although traditional security mechanisms can be implemented at the IT-infrastructure level of such cyber-physical systems, the community has acknowledged that it is imperative to also monitor the process-level activity, as attacks on ICS may very well influence the physical process. In this paper, we present PASAD, a novel stealthy-attack detection mechanism that monitors time series of sensor measurements in real time for structural changes in the process behavior. We demonstrate the effectiveness of our approach through simulations and experiments on data from real systems. Experimental results show that PASAD is capable of detecting not only significant deviations in the process behavior, but also subtle attack-indicating changes, significantly raising the bar for strategic adversaries who may attempt to maintain their malicious manipulation within the noise level.
Mammalian behavior and physiology converge to confirm sharper cochlear tuning in humans

Significance Sound consists of a dynamic stream of energy at different frequencies. Auditory processing of sound frequency is critical in determining our ability to interact and communicate in a complex acoustic world, yet fundamental gaps remain in our understanding of how this is achieved. Indeed, the resolving power of the system, how best to measure it, and the mechanisms that underlie it are all still debated. Here, we provide critical evidence demonstrating that humans can resolve the frequency components of competing sounds better than other commonly studied mammals. This finding raises important questions both for theories of auditory perception and for our understanding of the evolutionary relationships between the auditory system and acoustic communication, including speech. Frequency analysis of sound by the cochlea is the most fundamental property of the auditory system. Despite its importance, the resolution of this frequency analysis in humans remains controversial. The controversy persists because the methods used to estimate tuning in humans are indirect and have not all been independently validated in other species. Some data suggest that human cochlear tuning is considerably sharper than that of laboratory animals, while others suggest little or no difference between species. We show here in a single species (ferret) that behavioral estimates of tuning bandwidths obtained using perceptual masking methods, and objective estimates obtained using otoacoustic emissions, both also employed in humans, agree closely with direct physiological measurements from single auditory-nerve fibers. Combined with human behavioral data, this outcome indicates that the frequency analysis performed by the human cochlea is of significantly higher resolution than found in common laboratory animals. This finding raises important questions about the evolutionary origins of human cochlear tuning, its role in the emergence of speech communication, and the mechanisms underlying our ability to separate and process natural sounds in complex acoustic environments.
Re: Indication and survival bias: Threats to Resuscitative Endovascular Balloon Occlusion of the Aorta (REBOA) outcomes research.

studied, andwe are therefore unable to comment if there were any management differences between the trauma and pediatric groups. We are unable to remediate potential time bias since pediatric surgeons performed 87% of the appendectomies from 2007 to 2011 and 1.8% from 2012 to 2016. As acknowledged in the article, we agree that care during a later timeframe may have contributed to decreased length of stay. However, we respectfully disagree that this finding is “entirely reflective of this being a more contemporary cohort. ”Again, time from diagnosis to surgery was a median of 104minutes shorter in the trauma group, despite significantly more patients in the trauma group needing to be transferred before surgery, which added approximately 2 hours to the stay. As described in the article, the dictated operative report was used to define the presence of appendicitis. Using this definition, there were no negative appendectomies in the 220 studied patients. To better respond to Polites et al.'s concern, all pathology reports were subsequently reviewed, showing 6 (2.7%) pathologically normal appendices. There was no difference between the trauma and pediatric groups. We respectfully stand by our conclusions that trauma surgeon performance of emergency appendectomy in 5to 10-year-old children decreased length of hospital staywith similar complication rates as compared with pediatric surgeons.
Wnt signaling dynamics in head and neck squamous cell cancer tumor‐stroma interactions

Wnt pathway activation maintains the cancer stem cell (CSC) phenotype and promotes tumor progression, making it an attractive target for anti‐cancer therapy. Wnt signaling at the tumor and tumor microenvironment (TME) front have not been investigated in depth in head and neck squamous cell carcinoma (HNSCC). In a cohort of 48 HNSCCs, increased Wnt signaling, including Wnt genes (AXIN2, LGR6, WISP1) and stem cell factors (RET, SOX5, KIT), were associated with a more advanced clinical stage. Key Wnt pathway proteins were most abundant at the cancer epithelial‐stromal boundary. To investigate these observations, we generated three pairs of cancer‐cancer associated fibroblast (CAF) cell lines derived from the same HNSCC patients. 3D co‐culture of cancer spheres and CAFs mimicked these in vivo interactions, and using these we observed increased expression of Wnt genes (eg, WNT3A, WNT7A, WNT16) in both compartments. Of these Wnt ligands, we found Wnt3a, and less consistently Wnt16, activated Wnt signaling in both cancer cells and CAFs. Wnt activation increased CSC characteristics like sphere formation and invasiveness, which was further regulated by the presence of CAFs. Time lapse microscopy also revealed preferential Wnt activation of cancer cells. Wnt inhibitors, OMP‐18R5 and OMP‐54F28, significantly reduced growth of HNSCC patient‐derived xenografts and suppressed Wnt activation at the tumor epithelial‐stromal boundary. Taken together, our findings suggest that Wnt signaling is initiated in cancer cells which then activate CAFs, and in turn perpetuate a paracrine signaling loop. This suggests that targeting Wnt signaling in the TME is essential.