Miracle-dz/UniFAR-FTUs huggingface dataset:
KIST

Tor’s growing popularity and user diversity has resulted in network performance problems that are not well understood, though performance is understood to be a significant factor in Tor’s security. A large body of work has attempted to solve performance problems without a complete understanding of where congestion occurs in Tor. In this article, we first study congestion in Tor at individual relays as well as along the entire end-to-end Tor path and find that congestion occurs almost exclusively in egress kernel socket buffers. We then analyze Tor’s socket interactions and discover two major contributors to Tor’s congestion: Tor writes sockets sequentially, and Tor writes as much as possible to each socket. To improve Tor’s performance, we design, implement, and test KIST: a new socket management algorithm that uses real-time kernel information to dynamically compute the amount to write to each socket while considering all circuits of all writable sockets when scheduling cells. We find that, in the medians, KIST reduces circuit congestion by more than 30%, reduces network latency by 18%, and increases network throughput by nearly 10%. We also find that client and relay performance with KIST improves as more relays deploy it and as network load and packet loss rates increase. We analyze the security of KIST and find an acceptable performance and security tradeoff, as it does not significantly affect the outcome of well-known latency, throughput, and traffic correlation attacks. KIST has been merged and configured as the default socket scheduling algorithm in Tor version 0.3. 2.1-alpha (released September 18, 2017) and became stable in Tor version 0.3. 2.9 (released January 9, 2018). While our focus is Tor, our techniques and observations should help analyze and improve overlay and application performance, both for security applications and in general.
Gaussian Highpass Filters-based Convolutional Neural Network for Pulmonary Nodules Detection in CT Images

The segmentation of various types of nodules in CT images presents various challenges due to a large amount of information that needs to be processed. In this study, we proposed a Gaussian highpass filter-based convolutional neural network(CNN) for the fully-automated detection of pulmonary nodules in CT scans. In medical image analysis, the dataset sizes are usually too small to train the network. Therefore, for each training data, a set of 2-D patches from differently oriented planes are extracted. The extracted datasets are used as inputs for the proposed framework which comprises multiple streams of 2-D CNN, and the obtained outputs are combined to produce the final classification. We evaluate this strategy on a test set of 888 CT scans and compare it with other CNN or published methodologies using the same dataset. The results indicate that the proposed framework offers significant performance gains over other methods.
A Close Look at a Daily Dataset of Malware Samples

The number of unique malware samples is growing out of control. Over the years, security companies have designed and deployed complex infrastructures to collect and analyze this overwhelming number of samples. As a result, a security company can collect more than 1M unique files per day only from its different feeds. These are automatically stored and processed to extract actionable information derived from static and dynamic analysis. However, only a tiny amount of this data is interesting for security researchers and attracts the interest of a human expert. To the best of our knowledge, nobody has systematically dissected these datasets to precisely understand what they really contain. The security community generally discards the problem because of the alleged prevalence of uninteresting samples. In this article, we guide the reader through a step-by-step analysis of the hundreds of thousands Windows executables collected in one day from these feeds. Our goal is to show how a company can employ existing state-of-the-art techniques to automatically process these samples and then perform manual experiments to understand and document what is the real content of this gigantic dataset. We present the filtering steps, and we discuss in detail how samples can be grouped together according to their behavior to support manual verification. Finally, we use the results of this measurement experiment to provide a rough estimate of both the human and computer resources that are required to get to the bottom of the catch of the day.
Knee Articular Cartilage Segmentation from MR Images

Articular cartilage (AC) is a flexible and soft yet stiff tissue that can be visualized and interpreted using magnetic resonance (MR) imaging for the assessment of knee osteoarthritis. Segmentation of AC from MR images is a challenging task that has been investigated widely. The development of computational methods to segment AC is highly dependent on various image parameters, quality, tissue structure, and acquisition protocol involved. This review focuses on the challenges faced during AC segmentation from MR images followed by the discussion on computational methods for semi/fully automated approaches, whilst performances parameters and their significances have also been explored. Furthermore, hybrid approaches used to segment AC are reviewed. This review indicates that despite the challenges in AC segmentation, the semi-automated method utilizing advanced computational methods such as active contour and clustering have shown significant accuracy. Fully automated AC segmentation methods have obtained moderate accuracy and show suitability for extensive clinical studies whilst advanced methods are being investigated that have led to achieving significantly better sensitivity. In conclusion, this review indicates that research in AC segmentation from MR images is moving towards the development of fully automated methods using advanced multi-level, multi-data, and multi-approach techniques to provide assistance in clinical studies.
Non-Rigid Point Set Registration via Gaussians Mixture Model with Local Constraints

The local feature of point set is as important as the global feature in the point set registration problem. In this paper, a non-rigid point set registration method based on probability model with local constraints was proposed. Firstly, Gaussian mixture model (GMM) is used to determine the global relationship between two point sets. Secondly, local constraints provided by k nearest neighbor points helps to estimate the transformation better. Thirdly, the transformation of two point sets is calculated in reproducing kernel Hilbert space (RKHS). Finally, expectation maximization (EM) algorithm is used for maximum likelihood estimation of parameters in our method. Comparative experiments on synthesized data show that our algorithm is more robust to distortion, such as deformation, noise and outlier. Our method is also applied to the retinal image registration and obtained very good results.
Virtual assistants for mobile interaction: a review from the accessibility perspective

The technology of virtual assistants (VAs) is a powerful option to support the interaction of human users with computational systems. These VAs are able, for example, to identify interaction problems and offer recommendations on the execution of commands. This work analyses the use of VAs as a resource of accessibility for mobile devices. This analysis was carried out by means of a literature review, which considered both academic studies and commercial solutions. The results showed that there are very few researches in this area and this fact motivated the development of an evaluation protocol, and related set of tests cases, which can verify if current VAs are in fact able to support the interaction of motor and visually impaired users with their mobile devices.
Curricular and Learning Strategies for the Successful Implementation of STEM Education in Primary and Secondary Schools

STEM education was created as a unifying curricular paradigm in which students were educated in four distinct disciplines: science, technology, engineering, and mathematics, in an interdisciplinary and integrated way. First developed in USA, STEM education has been attempted in several countries, both in the public and the private education systems. The data, however, shows that the number of students interested in these topics is small and decreasing, with special impact on women and minorities. This paper summarizes the main trends in the integration of STEM in school systems, and identifies the three fundamental obstacles for its successful implementation: a) the curriculum in math is obsolete, and therefore physics, chemistry and biology are studied with obsolete tools and perspective; b) students are not educated in the resolution of complex problems that require higher level mental processes, involving higher level cognitive modules, globally known as System-1; and c) students still use pencil and paper to solve problems, while complex problems need for their resolution and iterative process of experimentation and discovery that requires external micro-worlds or ecosystems for their implementation and manipulation. This paper also proposes a set of curricular strategies to address these three fundamental obstacles. Specifically, this paper presents six areas of interdisciplinary curricular development for the successful integration of STEM education. A set of examples are used to illustrate the main constituents of these strategies and the implications on students' performance.
Study of socially appropriate robot behaviors in human-robot conversation closure

In human-robot communication, humans appear to have difficulty ending their conversations with robots due to a certain consideration toward the robot itself. Thus, verbal and nonverbal cues in robot behaviors are critical to ease conversational closure. In this study1, human participants observed either of three types of robot nonverbal behaviors, e.g., continual gestures by robot(Condition 1), reducing gestures(Condition 2) and gesture followed by adapter(Condition 3). All the conversations were videotaped, and the participants were interviewed after the experiment. The experimental results showed that adapter behaviors of robot helped to end a conversation. Furthermore, when the robot expressed adapters, the human followed suit, indicating that the participants wanted to end the conversation. These findings will contribute to the design of robot behavior that improves human-robot communication.
Detecting and Assessing Anomalous Evolutionary Behaviors of Nodes in Evolving Social Networks

Based on the performance of entire social networks, anomaly analysis for evolving social networks generally ignores the otherness of the evolutionary behaviors of different nodes, such that it is difficult to precisely identify the anomalous evolutionary behaviors of nodes (AEBN). Assuming that a node's evolutionary behavior that generates and removes edges normally follows stable evolutionary mechanisms, this study focuses on detecting and assessing AEBN, whose evolutionary mechanisms deviate from their past mechanisms, and proposes a link prediction detection (LPD) method and a matrix perturbation assessment (MPA) method. LPD describes a node's evolutionary behavior by fitting its evolutionary mechanism, and designs indexes for edge generation and removal to evaluate the extent to which the evolutionary mechanism of a node's evolutionary behavior can be fitted by a link prediction algorithm. Furthermore, it detects AEBN by quantifying the differences among behavior vectors that characterize the node's evolutionary behaviors in different periods. In addition, MPA considers AEBN as a perturbation of the social network structure, and quantifies the effect of AEBN on the social network structure based on matrix perturbation analysis. Extensive experiments on eight disparate real-world networks demonstrate that analyzing AEBN from the perspective of evolutionary mechanisms is important and beneficial.
What Drives Cryptocurrency Prices?

The Google Trends1 search analysis service and the Telegram2 messaging platform are investigated to determine their respective relationships to cryptocurrency price behaviour. It is shown that, in contrast to earlier findings, the relationship between cryptocurrency price movements and internet search volumes obtained from Google Trends is no longer consistently positive, with strong negative correlations detected for Bitcoin and Ethereum during June 2018. Sentiment extracted from cryptocurrency investment groups on Telegram is found to be positively correlated to Bitcoin and Ethereum price movements, particularly during periods of elevated volatility. The number of messages posted on a Bitcoin-themed Telegram group is found to be an indicator of Bitcoin price action in the subsequent week. A long shortterm memory (LSTM) recurrent neural network is developed to predict the direction of cryptocurrency prices using data obtained from Google Trends and Telegram. It is shown that Telegram data is a better predictor of the direction of the Bitcoin market than Google Trends. The converse is true for Ethereum. The LSTM model produces the most accurate results when predicting price movements over a one-week period.
Analysing micro-location beacon gamification: scenarios, types and characteristics

Gamification applied to service marketing is a growing area of research with an increasing focus on physical location and service. However, there are often accuracy issues with GPS implementations of location-based gamification. In this problem domain, micro-location has emerged. This paper proposes an analysis method to the theme and categorises micro-located gamification applications in an effort to understand the capabilities, advantages and shortcomings of the technology. Data were gathered from 30 micro-located gamification applications between the years of 2013 and 2018. The data were analysed through relational content analysis allowing categorisation and theme identification. Various scenarios are presented where micro-location plays a significant HCI role. Examples of existing services that implement gamification are also presented. Finally, factors that impact the micro-location method are explored. These findings contribute to HCI by providing guidance for present and perspective micro-location gamification implementations.
Telemonitoring of Blood Glucose: A Prototype Android Application Enhancing the Patient / Health Professional Experience Using Health IT Communication Standards

The smartphone, as one of the most personalized digital devices of our time, can play an important role in healthcare related matters since it can route health information gained at the patients' side to the care giving health professionals. One use case is the telemonitoring of blood glucose concentrations from type 2 diabetes patients. Since multiple blood glucose meters for the consumer market exist and health professionals operate different information systems standardized communication interfaces are needed. Specifications for those interfaces are available from the Personal Connected Health Alliance. This work introduces an Android application that is able to communicate with blood glucose monitor devices over Bluetooth Low Energy and forward the received and stored blood glucose readings in form of a HL7 Clinical Document Architecture (CDA) document. The created CDA document conforms to HL7 and IHE specifications and is send encrypted and digitally signed via email according to IHE XDM requirements. It could be shown, that the requirements from different standard defining organizations can be implemented in an Android application using stock Android features.
An interview with Pamela Wisniewski

Dr. Pamela Wisniewski is an assistant professor at the University of Central Florida's Department of Computer Science and an inaugural member of the ACM Future Computing Academy. As a human-computer interaction researcher, she studies privacy as a means to protect people, but more importantly, as a social mechanism to enrich online interactions that people share with others. She is particularly interested in the interplay between social media, privacy, and online safety for adolescents. Being a survivor of childhood sexual abuse, she is committed to protecting at-risk youth from online sexual predation risks, as well as empowering vulnerable youth online, so that they can garner the resources and support they need to overcome adversity and succeed in life.
A Hybrid Approach for Improving the Design Quality of Web Service Interfaces

A key success of a Web service is to appropriately design its interface to make it easy to consume and understand. In the context of service-oriented computing (SOC), the service’s interface is the main source of interaction with the consumers to reuse the service functionality in real-world applications. The SOC paradigm provides a collection of principles and guidelines to properly design services to provide best practice of third-party reuse. However, recent studies showed that service designers tend to pay little care to the design of their service interfaces, which often lead to several side effects known as antipatterns. One of the most common Web service interface antipatterns is to expose a large number of semantically unrelated operations, implementing different abstractions, in one single interface. Such bad design practices may have a significant impact on the service reusability, understandability, as well as the development and run-time characteristics. To address this problem, in this article, we propose a hybrid approach to improve the design quality of Web service interfaces and fix antipatterns as a combination of both deterministic and heuristic-based approaches. The first step consists of a deterministic approach using a graph partitioning-based technique to split the operations of a large service interface into more cohesive interfaces, each one representing a distinct abstraction. Then, the produced interfaces will be checked using a heuristic-based approach based on the non-dominated sorting genetic algorithm (NSGA-II) to correct potential antipatterns while reducing the interface design deviation to avoid taking the service away from its original design. To evaluate our approach, we conduct an empirical study on a benchmark of 26 real-world Web services provided by Amazon and Yahoo. Our experiments consist of a quantitative evaluation based on design quality metrics, as well as a qualitative evaluation with developers to assess its usefulness in practice. The results show that our approach significantly outperforms existing approaches and provides more meaningful results from a developer’s perspective.
Automatic Vectorization of Stencil Codes with the GGDML Language Extensions

Partial differential equation (PDE) solvers are important for many applications. PDE solvers execute kernels which apply stencil operations over 2D and 3D grids. As PDE solvers and stencil codes are widely used in performance critical applications, they must be well optimized. Stencil computations naturally depend on neighboring grid elements. Therefore, data locality must be exploited to optimize the code and to better use the memory bandwidth -- at the same time, vector processing capabilities of the processor must be utilized. In this work, we investigate the effectiveness of using high-level language extensions to exploit SIMD and vectorization features of multicore processors and vector engines. We write a prototype application using the GGDML high-level language extensions, and translate the high-level code with different configurations to investigate the efficiency of the language extensions and the source-to-source translation process to exploit the vector units of the multi-core processors and the vector engines. The conducted experiments demonstrate the effectiveness of the language extensions and the translation tool to generate vectorized codes, which makes use of the natural data locality of stencil computations.
Understanding two graphical visualizations from observer's pupillary responses and neural network

This paper investigates observers' pupillary responses while they viewed two graphical visualizations (circular and organizational). The graphical visualizations are snapshots of the kind of data used in checking the degree of compliance with corporate governance best practice. Six very similar questions were asked from 24 observers for each visualization. In particular, we developed a neural network based classification model to understand these two visualizations from temporal features of observers' pupillary responses. We predicted that whether each observer is more accurate in understanding the two visualizations from their unconscious pupillary responses or conscious verbal responses, by answering relevant questions. We found that observers were physiologically 96.5% and 95.1% accurate, and verbally 80.6% and 81.3% accurate, for the circular and organizational visualizations, respectively.
HandsInTouch: sharing gestures in remote collaboration

Many systems have been developed to support remote collaboration, where hand gestures or sketches can be shared. However, the effect of combining gesture and sketching together has not been fully explored and understood. In this paper we describe HandsInTouch, a system in which both hand gestures and sketches made by a remote helper are shown to a local user in real time. We conducted a user study to test the usability of the system and the usefulness of combing gesture and sketching for remote collaboration. We discuss results and make recommendations for system design and future work.1
How the inclusion of design principles contribute to the development of social robots

Knowledge about how social robots should be designed is fragmented, and there is currently limited discussion around developing a consistent body of knowledge to describe the design process of these social machines. This paper describes eleven design principles for the development of interactions between humans and robots including programming practices, characteristics of robots as products, interactive behaviours, and even moral values for users. We draw inspiration from the development and design practices of a range of interactive systems and several fields of design that inform the interdisciplinary potential of social robotics. Finally, we propose the empirical validation of these design principles in future work.