Miracle-dz/UniFAR-FTUs huggingface dataset:
KnightKing: a fast distributed graph random walk engine

Random walk on graphs has recently gained immense popularity as a tool for graph data analytics and machine learning. Currently, random walk algorithms are developed as individual implementations and suffer significant performance and scalability problems, especially with the dynamic nature of sophisticated walk strategies. We present KnightKing, the first general-purpose, distributed graph random walk engine. To address the unique interaction between a static graph and many dynamic walkers, it adopts an intuitive walker-centric computation model. The corresponding programming model allows users to easily specify existing or new random walk algorithms, facilitated by a new unified edge transition probability definition that applies across popular known algorithms. With KnightKing, these diverse algorithms benefit from its common distributed random walk execution engine, centered around an innovative rejection-based sampling mechanism that dramatically reduces the cost of higher-order random walk algorithms. Our evaluation confirms that KnightKing brings up to 4 orders of magnitude improvement in executing algorithms that currently can only be afforded with approximation solutions on large graphs.
Accurate and Low-Latency Sensing of Touch Contact on Any Surface with Finger-Worn IMU Sensor

Head-mounted Mixed Reality (MR) systems enable touch interaction on any physical surface. However, optical methods (i.e., with cameras on the headset) have difficulty in determining the touch contact accurately. We show that a finger ring with Inertial Measurement Unit (IMU) can substantially improve the accuracy of contact sensing from 84.74% to 98.61% (f1 score), with a low latency of 10 ms. We tested different ring wearing positions and tapping postures (e.g., with different fingers and parts). Results show that an IMU-based ring worn on the proximal phalanx of the index finger can accurately sense touch contact of most usable tapping postures. Participants preferred wearing a ring for better user experience. Our approach can be used in combination with the optical touch sensing to provide robust and low-latency contact detection.
TransVoice: Real-Time Voice Conversion for Augmenting Near-Field Speech Communication

Despite promising initial studies, a speaker's original voice can cause problems when it comes to the application of real-time voice conversion (data-driven speaker conversion) technology in our daily lives, specifically in our near-field communication, because the overlapping speech degrades the sense of immersion to the converted speech. We present TransVoice, a real-time voice conversion system that physically confines original speech with a mask-shaped device. Our preliminary study shows the proposed device can reduce the volume of original speech significantly, while it ameliorates the deteriorated conversion quality of the deep neural network (DNN) thanks to an integrated filter that weakens the low frequency range. We discuss novel applications using TransVoice that can augment our communication.
Mise-Unseen: Using Eye Tracking to Hide Virtual Reality Scene Changes in Plain Sight

Creating or arranging objects at runtime is needed in many virtual reality applications, but such changes are noticed when they occur inside the user's field of view. We present Mise-Unseen, a software system that applies such scene changes covertly inside the user's field of view. Mise-Unseen leverages gaze tracking to create models of user attention, intention, and spatial memory to determine if and when to inject a change. We present seven applications of Mise-Unseen to unnoticeably modify the scene within view (i) to hide that task difficulty is adapted to the user, (ii) to adapt the experience to the user's preferences, (iii) to time the use of low fidelity effects, (iv) to detect user choice for passive haptics even when lacking physical props, (v) to sustain physical locomotion despite a lack of physical space, (vi) to reduce motion sickness during virtual locomotion, and (vii) to verify user understanding during story progression. We evaluated Mise-Unseen and our applications in a user study with 15 participants and find that while gaze data indeed supports obfuscating changes inside the field of view, a change is rendered unnoticeably by using gaze in combination with common masking techniques.
Lineage stash: fault tolerance off the critical path

As cluster computing frameworks such as Spark, Dryad, Flink, and Ray are being deployed in mission critical applications and on larger and larger clusters, their ability to tolerate failures is growing in importance. These frameworks employ two broad approaches for fault tolerance: checkpointing and lineage. Checkpointing exhibits low overhead during normal operation but high overhead during recovery, while lineage-based solutions make the opposite tradeoff. We propose the lineage stash, a decentralized causal logging technique that significantly reduces the runtime overhead of lineage-based approaches without impacting recovery efficiency. With the lineage stash, instead of recording the task's information before the task is executed, we record it asynchronously and forward the lineage along with the task. This makes it possible to support large-scale, low-latency (millisecond-level) data processing applications with low runtime and recovery overheads. Experimental results for applications in distributed training and stream processing show that the lineage stash provides task execution latencies similar to checkpointing alone, while incurring a recovery overhead as low as traditional lineage-based approaches.
Tracking recurring concepts from evolving data streams using ensemble method

: Ensemble models are the most widely used methods for classifying evolving data stream. However, most of the existing data stream ensemble classification algorithms do not consider the issue of recurring concepts, which commonly exist in real-world applications. Motivated by this challenge, an Ensemble with internal Change Detection (ECD) was proposed to enhance performance by exploring the recurring concepts. It is done by maintaining a pool of classifiers, which dynamically adds and removes classifiers in response to the change detector. The algorithm adopts a two window change detection model, which adopts the Jensen-Shannon divergence to measure the distance of the distributions between old and recent data. When a change is detected, the repository of stored historical concepts is checked for reuse. Experimental results on both synthetic and real-world data streams demonstrate that the proposed algorithm not only outperforms the state-of-art methods on standard evaluation metrics, but also adapts well in different types of concept drift scenarios especially when concept s reappear.
Parameter optimization of single sample virtually expanded method

Aiming at single sample experiment of sample number n=1, the relationship of parameters virtually expanded from n=1 to n=13 is derived in this paper,and the big-samples data are gained by Bootstrap method. Instead of existing methods, a developing particle swarm optimization based on Minimax is put forward. With the application of this method in the parameter optimization, the lower confidence limit approaches the lower confidence limit of the Semiempirical Evaluation Method with more rapid speed and higher precision. In this way, the most suitable augmented parameters virtually expanded from n=1 to n=13 are gained, which provides a better virtual augment method for the sample augment from n=1 to n=13.
What You See is NOT What You Get: Discovering and Tracking Social Engineering Attack Campaigns

Malicious ads often use social engineering (SE) tactics to coax users into downloading unwanted software, purchasing fake products or services, or giving up valuable personal information. These ads are often served by low-tier ad networks that may not have the technical means (or simply the will) to patrol the ad content they serve to curtail abuse. In this paper, we propose a system for large-scale automatic discovery and tracking of SE Attack Campaigns delivered via Malicious Advertisements (SEACMA). Our system aims to be generic, allowing us to study the SEACMA ad distribution problem without being biased towards specific categories of ad-publishing websites or SE attacks. Starting with a seed of low-tier ad networks, we measure which of these networks are the most likely to distribute malicious ads and propose a mechanism to discover new ad networks that are also leveraged to support the distribution of SEACMA campaigns. The results of our study aim to be useful in a number of ways. For instance, we show that SEACMA ads use a number of tactics to successfully evade URL blacklists and ad blockers. By tracking SEACMA campaigns, our system provides a mechanism to more proactively detect and block such evasive ads. Therefore, our results provide valuable information that could be used to improve defense systems against social engineering attacks and malicious ads in general.
Ohmic-Sticker: Force-to-Motion Type Input Device that Extends Capacitive Touch Surface

We propose "Ohmic-Sticker'', a novel force-to-motion type input device to extend capacitive touch surfaces. It realizes various types of force-sensitive inputs by simply attaching on to commercial touchpads or touchscreens. A simple force-sensitive-resistor (FSR)-based structure enables thin (less than 2 mm) form factors and battery-less operation. The applied force vector is detected as the leakage current from the corresponding touch surface electrodes by using Ohmic-Touch technology. Ohmic-Sticker can be used for adding force-sensitive interactions to touch surfaces, such as analog push buttons, TrackPoint-like devices, and full 6 DoF controllers for navigating virtual spaces. In this paper, we report a series of investigations on the design requirements of Ohmic-Sticker and some prototypes.We also evaluate the performance of Ohmic-Sticker as a pointing device.
Towards Tailoring Digital Food Labels: Insights of a Smart-RCT on User-specific Interpretation of Food Composition Data

Front-of-pack nutrition labelling (FoPL) supports healthier food choices yet remain unmandated in most countries. Simultaneously, labels are criticized for giving standardized recommendations that overlook individual needs. To assess the potential of consumer-specific tailored labels, we thus developed and tested a tailoring logic for adapting labels to individual dietary requirements and a smartphone app that then provided tailored food labels after scanning a product's barcode. The tailoring logic was developed with dieticians, accounting for gender, age, activity, preferences, diet-related diseases. The label showed a combination of established labelling systems: Nutri-Score and Multiple Traffic Light. The application followed a smart-RCT design, randomly attributing users either with tailored or standardized labels. 33 users met the eligibility criteria for our exploratory study. We found promising evidence that tailored digital food labels are perceived as more helpful, relevant, and recommendable than current static food labels, especially in the absence of FoPL.
Tales from the Porn: A Comprehensive Privacy Analysis of the Web Porn Ecosystem

Modern privacy regulations, including the General Data Protection Regulation (GDPR) in the European Union, aim to control user tracking activities in websites and mobile applications. These privacy rules typically contain specific provisions and strict requirements for websites that provide sensitive material to end users such as sexual, religious, and health services. However, little is known about the privacy risks that users face when visiting such websites, and about their regulatory compliance. In this paper, we present the first comprehensive and large-scale analysis of 6,843 pornographic websites. We provide an exhaustive behavioral analysis of the use of tracking methods by these websites, and their lack of regulatory compliance, including the absence of age-verification mechanisms and methods to obtain informed user consent. The results indicate that, as in the regular web, tracking is prevalent across pornographic sites: 72% of the websites use third-party cookies and 5% leverage advanced user fingerprinting technologies. Yet, our analysis reveals a third-party tracking ecosystem semi-decoupled from the regular web in which various analytics and advertising services track users across, and outside, pornographic websites. We complete the paper with a regulatory compliance analysis in the context of the EU GDPR, and newer legal requirements to implement verifiable access control mechanisms (e.g., UK's Digital Economy Act). We find that only 16% of the analyzed websites have an accessible privacy policy and only 4% provide a cookie consent banner. The use of verifiable access control mechanisms is limited to prominent pornographic websites.
Efficient multiple pattern matching algorithm based on BMH: MP-BMH

: String matching is playing a key role in a wide range of applications in the information computing. Due to its importance, large numbers of different algorithms have been developed over the last 50 years. There are various standards of single and multiple pattern string matching algorithms like Boyer-Moore (BM), Boyer-Moore-Horspool (BMH), Aho-Corasick etc. Multiple pattern string matching is very useful in real world applications. Standard benchmark multiple pattern algorithm Aho-Corasick and many other multiple pattern string matching algorithms are not memory and time efficient for wider length and large number of patterns set on large text data set because they are using the concept like DFA and require full scan of text data. Many string matching tools which are currently popular for string matching are based on these algorithms. Using the bad character principle of BMH, a method for multiple pattern string matching is being developed. Using this method a time and memory efficient exact multiple pattern string matching algorithm Multiple Pattern BMH (MP-BMH) is proposed. Unlike Aho-Corasick, the proffered MP-BMH algorithm provides skipping of unnecessary matching of characters in text while searching like BMH Algorithm. It also provides the skipping of characters in patterns using the similarity between patterns. Along with the shifts while searching, this algorithm also provides shrewd switches among the patterns for efficacious matching.In this paper, the aforesaid method, MP-BMH algorithm with its time, memory and experimental analysis are described.
KVell: the design and implementation of a fast persistent key-value store

Modern block-addressable NVMe SSDs provide much higher bandwidth and similar performance for random and sequential access. Persistent key-value stores (KVs) designed for earlier storage devices, using either Log-Structured Merge (LSM) or B trees, do not take full advantage of these new devices. Logic to avoid random accesses, expensive operations for keeping data sorted on disk, and synchronization bottlenecks make these KVs CPU-bound on NVMe SSDs. We present a new persistent KV design. Unlike earlier designs, no attempt is made at sequential access, and data is not sorted when stored on disk. A shared-nothing philosophy is adopted to avoid synchronization overhead. Together with batching of device accesses, these design decisions make for read and write performance close to device bandwidth. Finally, maintaining an inexpensive partial sort in memory produces adequate scan performance. We implement this design in KVell, the first persistent KV able to utilize modern NVMe SSDs at maximum bandwidth. We compare KVell against available state-of-the-art LSM and B tree KVs, both with synthetic benchmarks and production workloads. KVell achieves throughput at least 2x that of its closest competitor on read-dominated workloads, and 5x on write-dominated workloads. For workloads that contain mostly scans, KVell performs comparably or better than its competitors. KVell provides maximum latencies an order of magnitude lower than the best of its competitors, even on scan-based workloads.
Aero-plane: A Handheld Force-Feedback Device that Renders Weight Motion Illusion on a Virtual 2D Plane

Force feedback is said to be the next frontier in virtual reality (VR). Recently, with consumers pushing forward with untethered VR, researchers turned away from solutions based on bulky hardware (e.g., exoskeletons and robotic arms) and started exploring smaller portable or wearable devices. However, when it comes to rendering inertial forces, such as when moving a heavy object around or when interacting with objects with unique mass properties, current ungrounded force feedback devices are unable to provide quick weight shifting sensations that can realistically simulate weight changes over 2D surfaces. In this paper we introduce Aero-plane, a force-feedback handheld controller based on two miniature jet propellers that can render shifting weights of up to 14 N within 0.3 seconds. Through two user studies we: (1) characterize the users' ability to perceive and correctly recognize different motion paths on a virtual plane while using our device; and, (2) tested the level of realism and immersion of the controller when used in two VR applications (a rolling ball on a plane, and using kitchen tools of different shapes and sizes). Lastly, we present a set of applications that further explore different usage cases and alternative form-factors for our device.
An analysis of performance evolution of Linux's core operations

This paper presents an analysis of how Linux's performance has evolved over the past seven years. Unlike recent works that focus on OS performance in terms of scalability or service of a particular workload, this study goes back to basics: the latency of core kernel operations (e.g., system calls, context switching, etc.). To our surprise, the study shows that the performance of many core operations has worsened or fluctuated significantly over the years. For example, the select system call is 100% slower than it was just two years ago. An in-depth analysis shows that over the past seven years, core kernel subsystems have been forced to accommodate an increasing number of security enhancements and new features. These additions steadily add overhead to core kernel operations but also frequently introduce extreme slowdowns of more than 100%. In addition, simple misconfigurations have also severely impacted kernel performance. Overall, we find most of the slowdowns can be attributed to 11 changes. Some forms of slowdown are avoidable with more proactive engineering. We show that it is possible to patch two security enhancements (from the 11 changes) to eliminate most of their overheads. In fact, several features have been introduced to the kernel unoptimized or insufficiently tested and then improved or disabled long after their release. Our findings also highlight both the feasibility and importance for Linux users to actively configure their systems to achieve an optimal balance between performance, functionality, and security: we discover that 8 out of the 11 changes can be avoided by reconfiguring the kernel, and the other 3 can be disabled through simple patches. By disabling the 11 changes with the goal of optimizing performance, we speed up Redis, Apache, and Nginx benchmark workloads by as much as 56%, 33%, and 34%, respectively.
TLS Beyond the Browser: Combining End Host and Network Data to Understand Application Behavior

The Transport Layer Security (TLS) protocol has evolved in response to different attacks and is increasingly relied on to secure Internet communications. Web browsers have led the adoption of newer and more secure cryptographic algorithms and protocol versions, and thus improved the security of the TLS ecosystem. Other application categories, however, are increasingly using TLS, but too often are relying on obsolete and insecure protocol options. To understand in detail what applications are using TLS, and how they are using it, we developed a novel system for obtaining process information from end hosts and fusing it with network data to produce a TLS fingerprint knowledge base. This data has a rich set of context for each fingerprint, is representative of enterprise TLS deployments, and is automatically updated from ongoing data collection. Our dataset is based on 471 million endpoint-labeled and 8 billion unlabeled TLS sessions obtained from enterprise edge networks in five countries, plus millions of sessions from a malware analysis sandbox. We actively maintain an open source dataset that, at 4,500+ fingerprints and counting, is both the largest and most informative ever published. In this paper, we use the knowledge base to identify trends in enterprise TLS applications beyond the browser: application categories such as storage, communication, system, and email. We identify a rise in the use of TLS by nonbrowser applications and a corresponding decline in the fraction of sessions using version 1.3. Finally, we highlight the shortcomings of naïvely applying TLS fingerprinting to detect malware, and we present recent trends in malware's use of TLS such as the adoption of cipher suite randomization.
PseudoBend: Producing Haptic Illusions of Stretching, Bending, and Twisting Using Grain Vibrations

We present PseudoBend, a haptic feedback technique that creates the illusion that a rigid device is being stretched, bent, or twisted. The method uses a single 6-DOF force sensor and a vibrotactile actuator to render grain vibrations to simulate the vibrations produced during object deformation based on the changes in force or torque exerted on a device. Because this method does not require any moving parts aside from the vibrotactile actuator, devices designed using this method can be small and lightweight. Psychophysical studies conducted using a prototype that implements this method confirmed that the method could be used to successfully create the illusion of deformation and could also change users' perception of stiffness by changing the virtual stiffness parameters.
Proxino: Enabling Prototyping of Virtual Circuits with Physical Proxies

We propose blending the virtual and physical worlds for prototyping circuits using physical proxies. With physical proxies, real-world components (e.g. a motor, or light sensor) can be used with a virtual counterpart for a circuit designed in software. We demonstrate this concept in Proxino, and elucidate the new scenarios it enables for makers, such as remote collaboration with physically distributed electronics components. We compared our hybrid system and its output with designs of real circuits to determine the difference through a system evaluation and observed minimal differences. We then present the results of an informal study with 9 users, where we gathered feedback on the effectiveness of our system in different working conditions (with a desktop, using a mobile, and with a remote collaborator). We conclude by sharing our lessons learned from our system and discuss directions for future research that blend physical and virtual prototyping for electronic circuits.
Finite State Transducer Calculus for Whole Word Morphology

The research on machine learning of morphology often involves formulating morphological descriptions directly on surface forms of words. As the established two-level morphology paradigm requires the knowledge of the underlying structure, it is not widely used in such settings. In this paper, we propose a formalism describing structural relationships between words based on theories of morphology that reject the notions of internal word structure and morpheme. The formalism covers a wide variety of morphological phenomena (including non-concatenative ones like stem vowel alternation) without the need of workarounds and extensions. Furthermore, we show that morphological rules formulated in such way can be easily translated to FSTs, which enables us to derive performant approaches to morphological analysis, generation and automatic rule discovery.