Miracle-dz/UniFAR-FTUs huggingface dataset:
Reliability of ADS-B communications: novel insights based on an experimental assessment

The Automatic Dependent Surveillance - Broadcast (ADS-B) technology promises to enhance the safety of civil avionics by diffusing flight data in a more efficient, timely, and easy to access fashion. Moreover, its adoption is mandatory by 2020. However, the quality of the communication is not completely satisfactory. Indeed, packets are lost for a number of reasons, such as obstacles, weather conditions, and by the fact that the frequency band on which ADS-B is intended to work in is the same shared by other legacy communication technologies used by the aircraft. Leveraging some previous work in the area providing preliminary study of packet loss issues in this specific context, in this paper we analyze the Opensky-network public database to provide more hints and real statistics on both the packet loss characterizing aircraft communications and the overall reliability of the ADS-B technology. Analyzing more than 21 GB of real aircraft-generated traces, we show that models introduced in the last years have severe limitations. This is imputable to several reasons, including the increased throughput and density of the network, as well as --- as discovered by our analysis --- that there is a not negligible portion of ADS-B implementations that do not follow the standard recommendation. Overall, this contribution intends to: (i) shed some lights on the current gap in the literature; (ii) provide a new, updated packet loss model for ADS-B communications; and, (iii) motivate further research efforts in the field, toward a precise characterization of the reliability of aircraft communications.
On the Challenges of Geographical Avoidance for Tor

Traffic-analysis attacks are a persisting threat for Tor users. When censors or law enforcement agencies try to identify users, they conduct traffic-confirmation attacks and monitor encrypted transmissions to extract metadata—in combination with routing attacks, these attacks become sufficiently powerful to de-anonymize users. While traffic-analysis attacks are hard to detect and expensive to counter in practice, geographical avoidance provides an option to reject circuits that might be routed through an untrusted area. Unfortunately, recently proposed solutions introduce severe security issues by imprudent design decisions. In this paper, we approach geographical avoidance starting from a thorough assessment of its challenges. These challenges serve as the foundation for the design of an empirical avoidance concept that considers actual transmission characteristics for justified decisions. Furthermore, we address the problems of untrusted or intransparent ground truth information that hinder a reliable assessment of circuits. Taking these features into account, we conduct an empirical simulation study and compare the performance of our novel avoidance concept with existing approaches. Our results show that we outperform existing systems by 22% fewer rejected circuits, which reduces the collateral damage of overly restrictive avoidance decisions. In a second evaluation step, we extend our initial system concept and implement the prototype TrilateraTor. This prototype is the first to satisfy the requirements of a practical deployment, as it maintains Tor’s original level of security, provides reasonable performance, and overcomes the fundamental security flaws of existing systems.
Cracking the Wall of Confinement: Understanding and Analyzing Malicious Domain Take-downs

Take-down operations aim to disrupt cybercrime involving malicious domains. In the past decade, many successful take-down operations have been reported, including those against the Conficker worm, and most recently, against VPNFilter. Although it plays an important role in fighting cybercrime, the domain take-down procedure is still surprisingly opaque. There seems to be no in-depth understanding about how the take-down operation works and whether there is due diligence to ensure its security and reliability. In this paper, we report the first systematic study on domain takedown. Our study was made possible via a large collection of data, including various sinkhole feeds and blacklists, passive DNS data spanning six years, and historical WHOIS information. Over these datasets, we built a unique methodology that extensively used various reverse lookups and other data analysis techniques to address the challenges in identifying taken-down domains, sinkhole operators, and take-down durations. Applying the methodology on the data, we discovered over 620K takendown domains and conducted a longitudinal analysis on the take-down process, thus facilitating a better understanding of the operation and its weaknesses. We found that more than 14% of domains taken-down over the past ten months have been released back to the domain market and that some of the released domains have been repurchased by the malicious actor again before being captured and seized, either by the same or different sinkholes. In addition, we showed that the misconfiguration of DNS records corresponding to the sinkholed domains allowed us to hijack a domain that was seized by the FBI. Further, we found that expired sinkholes have caused the transfer of around 30K takendown domains whose traffic is now under the control of new owners.
A first implementation and evaluation of the IEEE 802.11aa group addressed transmission service

The IEEE 802.11aa Task Group has recently standardized a set of mechanisms to efficiently support video multicasting, namely, the Group Addressed Transmission Service (GATS). In this article, we report the implementation of these mechanisms over commodity hardware, which we make publicly available, and conduct a study to assess their performance under a variety of real-life scenarios. To the best of our knowledge, this is the first experimental assessment of GATS, which is performed along three axes: we report their complexity in terms of lines of code, their effectiveness when delivering video traffic, and their efficiency when utilizing wireless resources. Our results provide key insights on the resulting trade-offs when using each mechanism, and paves the way for new enhancements to deliver video over 802.11 Wireless LANs.
ALDONA: a hybrid solution for sentence-level aspect-based sentiment analysis using a lexicalised domain ontology and a neural attention model

Sentences containing several different polarity aspects cause one of the main problems in sentiment analysis. Depending on an aspect, the same context words can have different effects on its sentiment value. Additionally, the polarity can be influenced by the domain-specific knowledge, showing the necessity to incorporate it into the sentiment classification. In this paper we present a hybrid solution for sentence-level aspect-based sentiment analysis using A Lexicalised Domain Ontology and Neural Attention (ALDONA) model to handle the problems mentioned above. To measure the influence of each word in a given sentence on an aspect's polarity, we introduce the bidirectional context attention mechanism. Moreover, the classification module is designed to handle the sentence's complex structure. Finally, the manually created lexicalised domain ontology (represented in OWL) is integrated to exploit the field-specific knowledge. Computational results obtained on a benchmark data set based on Web reviews have shown ALDONA's ability to outperform several state-of-the-art models and stress its contribution to aspect-based sentiment classification.
Graph sparsification with parallelization to optimize the identification of causal genes and dysregulated pathways

Disease causing genes and their pathways can be identified by mapping genetic interaction sequences into a biological network and performing graph traversals based on certain approach specific parameters. Considering the large size of biological networks, one would expect their network graphs to be sparse, since all large real-world graphs, like that of social networks, tend to be sparse. Gene interaction network graphs of complex organisms, however, are large and dense due to the large number of interactions in them, and their inherent complex nature. Computational approaches to the task encounter real-time challenges with processing and mining relevant information, as traversal algorithms may have multiple runs through the network itself, compounding the time taken. A large running time greatly restrains the number of genes that may be found within a reasonable time period. However, integrating graph parallel processing techniques provided by the MapReduce utility of Apache Hadoop, the execution time is brought down to a certain extent using a multi-node cluster, which is significant reduction in time but yet, not optimal. In this paper, existing algorithms used in the traversal of molecular interaction networks for the identification of causal genes and dysregulated pathways are optimized, by integrating graph sparsification with MapReduce computing paradigm for parallel processing, to identify relevant complex interaction sequences in a more efficient manner than the existing algorithms.
Mechanism for Measuring System Complexity Applying Sensitivity Analysis

This work proposes a complexity metric which maps internal connections of the system and its relationship with the environment through the application of sensitivity analysis. The proposed methodology presents (i) system complexity metric, (ii) system sensitivity metric, and (iii) two models as case studies. Based on the system dynamics, the complexity metric maps the internal connections through the states of the system and the metric of sensitivity evaluates the contribution of each parameter to the output variability. The models are simulated in order to quantify the complexity and the sensitivity and to analyze the behavior of the systems leading to the assumption that the system complexity is closely linked to the most sensitive parameters. As findings from results, it may be observed that systems may exhibit high performance as a result of optimized configurations given by their natural complexity.
Spectre without shared memory

The Spectre attack by Kocher et al. [11] reads arbitrary data from colocated processes by exploiting two common features of modern processors: speculative execution and shared caches. While theoretically the attack works in many different settings, the current variations all require that the attacker share with the target a memory region that includes vulnerable code which accepts input from the attacker. Motivated by the common practice in cloud computing of not allowing shared memory between different users, we construct the first Spectre type attack in which the target and the attacker do not share any memory pages. The target is a server and the attacker is colocated with the target, shares a Last-Level Cache with it and provides input to the target as a typical client over TCP. We develop new techniques for the attack including accurate location of the target's code and data in the shared cache, noise suppression enabling reliable retrieval of the target's data and optimizations speeding up the retrieval process. An indispensable tool in the retrieval process is a careful comparison of cache activity between two scenarios: the attacker sending as input an address of interest x and the attacker sending a different address x'. The comparison enables extraction of a single memory byte from the target. We report on a Proof-of-Concept implementation of our attack and on tests on two Intel multi-core platforms with inclusive Last-Level Caches and speculative execution. The tests ran in two virtualization settings, Virtual Machines and Linux containers and in two profiles of cache activity, relative inactivity and very high activity. The setup phase in which the attacker locates the target's data in the cache requires on the order of several minutes to several tens of minutes. The attack successfully extracts the data with probability per byte between 0.91 to 0.99 and rate ranging from 0.4 to 10 bytes per second.
The Effect of Lactic Acid Bacteria Isolates on the Urinary Tract Pathogens to Infants In Vitro

Urinary tract infections are common clinical problems in children, even though lots of treatment strategies have been tried. Many studies of the application of probiotics for urinary tract infection in female adults exist, but there is a lack of studies in children. The aims of this study were to screen probiotic strains for inhibiting the uropathogens in vitro, to find candidates for in vivo study. Nine strains of E. coli were isolated from children with urinary tract infection and six uropathogens were obtained from Korean Colletion for Type Cultures and American Type Culture Collection. Also 135 lactic acid bacteria (LAB) strains were isolated from healthy children, and were identified through physiologic, biochemical methods, 16S rDNA PCR, and data analysis. And with agar disk diffusion assay technique the antimicrobial activities of these LAB strains against those uropathogens were examined. Three strains of separated LAB strains demonstrated major antimicrobial activity against all the uropathogens. In the agar disk diffusion assay technique, antimicrobial activities increased most in the 4th day culture broth with separated Lactobacillus. In summary, some LAB can be used as candidates to develop the probiotic microorganisms that inhibit uropathogens in children, and are expected to be applied to treatment and prevention of pediatric urinary tract infection.
Stylus knife: improving cutting skill in paper-cutting by implementing pressure control

In this paper, we propose an interactive system for controlling the pressure while cutting paper with a knife. Our system supports skill improvement for novices by measuring and evaluating their cutting pressure in real time. In this study, we use a knife with a blade attached to a stylus with a pressure sensitive sensor, which can measure the pressure, coordinates, and cutting time. We compare the cutting pressure values of novices with those of artists and thereby observe the difference in their cutting skills. We have also developed a practice system that guides the novices on controlling the pressure by providing information on the cutting pressure values of experts. This system shows the difference in pressure between novices and experts using a synchronous display of color and sound. Using these functions, novices learn to adjust their cutting pressure according to that of experts. The purpose of this research is to improve the cutting skill of novices learning the art of paper-cutting. Instructors typically teach the novices how to control the cutting pressure during paper-cutting workshops, as novices tend to cut paper with an unstable and higher pressure than necessary. Determining the right cutting pressure is a critical skill in the art of paper-cutting, and we aim to improve the same with our system. We verify the effect of our system by practicing paper-cutting in various methods and comparing the cutting pressure values obtained. It is evident from the results that novices who practice with our system show progress on the average and variance of cutting pressures.
Cryptocurrency world identification and public concerns detection via social media: student research abstract

Cryptocurrency is one of the burning issues across the world in the modern era. Literature shows that business analysts always tend to use new technologies and investigate their risks. Some researchers predicted the price fluctuations and investigated the risk of cryptocurrency and blockchain network. However, to the best of our knowledge, there is no study which provides the feasibility information about locations for cryptocurrency investment around the world. This paper aims to provide the aforementioned information to the investors. The proposed methodology is based on Topic modeling along with public opinion mining about cryptocurrencies, blockchain network, bitcoin, litecoin, and ethereum. The other cryptocurrencies are not included in the study because of having insufficient relevant data on the social forums. Top locations are identified such as Australia, Denmark, Netherlands, and the USA etc. Apart from that, the public perception and environment feasibility are also determined. Almost 83.7% tweets of Sweden shows positive sentiment for cryptocurrency investment. Which ranks the highest having friendly environment for cryptocurrency investment. Similarly, the UK shows the least positive perception of cryptocurrency and blockchain technology usage. Some of the significant terms are also determined using Topic modeling from public opinions. Such as authorization rules, investment, profit, volatility, and security etc. These terms are almost identified as subtopic for each of the inquired keyword. This shows the mutual relationship among different cryptocurrencies and public's concerns about cryptocurrencies.
Development of intelligent robot hand using proximity, contact and slip sensing

To achieve the skillful task like the human, many researchers have been working on robot hand. An interaction with vision and tactile information are indispensable for realization of skillful tasks. In the existing research, the method using a camera to get the vision information is often found. But, in the boundary area of a non-contact phase and a contact phase, there are problem that lack of sensor information because the influence of occlusion comes up to surface. We devise to introduce the proximity sensor in this area. And we call the robot hand which is equipped with proximity, tactile and slip sensor “intelligent robot hand”. In this research, we show the constitution example of the intelligent robot hand and propose the method to realize Pick&Place as concrete task.
The posteriorizing gene Gbx2 is a direct target of Wnt signalling and the earliest factor in neural crest induction

Wnt signalling is required for neural crest (NC) induction; however, the direct targets of the Wnt pathway during NC induction remain unknown. We show here that the homeobox gene Gbx2 is essential in this process and is directly activated by Wnt/β-catenin signalling. By ChIP and transgenesis analysis we show that the Gbx2 regulatory elements that drive expression in the NC respond directly to Wnt/β-catenin signalling. Gbx2 has previously been implicated in posteriorization of the neural plate. Here we unveil a new role for this gene in neural fold patterning. Loss-of-function experiments using antisense morpholinos against Gbx2 inhibit NC and expand the preplacodal domain, whereas Gbx2 overexpression leads to transformation of the preplacodal domain into NC cells. We show that the NC specifier activity of Gbx2 is dependent on the interaction with Zic1 and the inhibition of preplacodal genes such as Six1. In addition, we demonstrate that Gbx2 is upstream of the neural fold specifiers Pax3 and Msx1. Our results place Gbx2 as the earliest factor in the NC genetic cascade being directly regulated by the inductive molecules, and support the notion that posteriorization of the neural folds is an essential step in NC specification. We propose a new genetic cascade that operates in the distinction between anterior placodal and NC territories.
Distribution and Diversity of Planktonic Fungi in the West Pacific Warm Pool

Fungi contribute substantially to biogeochemical cycles of terrestrial and marine habitats by decomposing matter and recycling nutrients. Yet, the diversity of their planktonic forms in the open ocean is poorly described. In this study, culture-independent and molecular approaches were applied to investigate fungal diversity and abundance derived from samples collected from a broad swath of the Pacific Warm Pool across major environmental gradients Our results revealed that planktonic fungi were molecularly diverse and their diversity patterns were related to major phytoplankton taxa and various nutrients including nitrate, nitrite, orthophosphate and silicic acid. Over 400 fungal phylotypes were recovered across this region and nearly half of them grouped into two major fungal lineages of Ascomycota and Basidiomycota, whose abundance varied among stations. These results suggest that planktonic fungi are a diverse and integral component of the marine microbial community and should be included in future marine microbial ecosystem models.
Geo-locating Drivers: A Study of Sensitive Data Leakage in Ride-Hailing Services

Increasingly, mobile application-based ride-hailing services have become a very popular means of transportation. Due to the handling of business logic, these services also contain a wealth of privacy-sensitive information such as GPS locations, car plates, driver licenses, and payment data. Unlike many of the mobile applications in which there is only one type of users, ride-hailing services face two types of users: riders and drivers. While most of the efforts had focused on the rider’s privacy, unfortunately, we notice little has been done to protect drivers. To raise the awareness of the privacy issues with drivers, in this paper we perform the first systematic study of the drivers’ sensitive data leakage in ride-hailing services. More specifically, we select 20 popular ride-hailing apps including Uber and Lyft and focus on one particular feature, namely the nearby cars feature. Surprisingly, our experimental results show that largescale data harvesting of drivers is possible for all of the ridehailing services we studied. In particular, attackers can determine with high-precision the driver’s privacy-sensitive information including mostly visited address (e.g., home) and daily driving behaviors. Meanwhile, attackers can also infer sensitive information about the business operations and performances of ride-hailing services such as the number of rides, utilization of cars, and presence on the territory. In addition to presenting the attacks, we also shed light on the countermeasures the service providers could take to protect the driver’s sensitive information.
Using design sprint as a facilitator in active learning for students in the requirements engineering course: an experience report

Design techniques focused in user are desirable for undergraduates, but there are few initiatives that address the insertion of these techniques into the curricula of Higher Education Brazilian's colleges and the Design Sprint seems be a good method for this, because of its structure and short time-box. This article sought to verify how Design Sprint behaves into reduced number of classes when it must generate a functional prototype. To that end, case study in exploratory research format was used. Reduced time was a complication factor in this adaptation, but in the survey applied, 91% of students considered Design Sprint productive in learning context and 100% of students agreed that Design Sprint can creatively solve problems. They also expressed a desire to participate in new applications of it for educational purposes. After the case study was planned, applied and validated, it is inferred that Design Sprint generate interesting insights that can be useful in a project-based learning context and students judge their own learning and participation to be positive in Design Sprint based classes.