Reviewerly/exHarmony huggingface dataset:
STEROIDS for DOPed Applications: A Compiler for Automated Data-Oriented Programming

The wide-spread adoption of system defenses such as the randomization of code, stack, and heap raises the bar for code-reuse attacks. Thus, attackers utilize a scripting engine in target programs like a web browser to prepare the code-reuse chain, e.g., relocate gadget addresses or perform a just-in-time gadget search. However, many types of programs do not provide such an execution context that an attacker can use. Recent advances in data-oriented programming (DOP) explored an orthogonal way to abuse memory corruption vulnerabilities and demonstrated that an attacker can achieve Turing-complete computations without modifying code pointers in applications. As of now, constructing DOP exploits requires a lot of manual work-for every combination of application and payload anew. In this paper, we present novel techniques to automate the process of generating DOP exploits. We implemented a compiler called STEROIDS that leverages these techniques and compiles our high-level language SLANG into low-level DOP data structures driving malicious computations at run time. This enables an attacker to specify her intent in an application-and vulnerability-independent manner to maximize reusability. We demonstrate the effectiveness of our techniques and prototype implementation by specifying four programs of varying complexity in SLANG that calculate the Levenshtein distance, traverse a pointer chain to steal a private key, relocate a ROP chain, and perform a JIT-ROP attack. STEROIDS compiles each of those programs to low-level DOP data structures targeted at five different applications including GStreamer, Wireshark and ProFTPd, which have vastly different vulnerabilities and DOP instances. Ultimately, this shows that our compiler is versatile, can be used for both 32-bit and 64-bit applications, works across bug classes, and enables highly expressive attacks without conventional code-injection or code-reuse techniques in applications lacking a scripting engine.
Neurally Controlled Graphic Equalizer

This paper describes a neural network based method to simplify the design of a graphic equalizer without sacrificing the accuracy of approximation. The key idea is to train a neural network to predict the mapping from target gains to the optimized band filter gains at specified center frequencies. The prediction is implemented with a feedforward neural network having a hidden layer with 20 neurons in the case of the ten-octave graphic equalizer. The band filter coefficients can then be quickly and easily computed using closed-form formulas. This work turns, for the first time, the accurate graphic equalization design into a feedforward calculation without matrix inversion or iterations. The filter gain control using the neural network reduces the computing time by 99.6% in comparison to the least-squares design method it is imitating and contributes an approximation error of less than 0.1 dB. The resulting neurally controlled graphic equalizer will be highly useful in various audio and music processing applications, which require time-varying equalization.
Named Entity Recognition in Traditional Chinese Medicine Clinical Cases Combining BiLSTM-CRF with Knowledge Graph

Named entity recognition in Traditional Chinese Medicine (TCM) clinical cases is a fundamental and crucial task for follow-up work. In recent years, deep learning approaches have achieved remarkable results in named entity recognition and other natural language processing tasks. However, these methods cannot effectively solve the problem of low recognition rate of rare words, which is common in TCM field. In this paper, we propose TCMKG-LSTM-CRF model that utilizes knowledge graph information to strength the learning ability and recognize rare words. This model introduces knowledge attention vector model to implement attention mechanism between hidden vector of neural networks and knowledge graph candidate vectors and consider influence from previous word. The experiment results prove the effectiveness of our model.
Lightweight Power Monitoring Framework for Virtualized Computing Environments

The pervasive use of virtualization techniques in today's datacenters poses challenges in power monitoring since it is not possible to directly measure the power consumption of a virtual entity such as a virtual machine (VM) and a container. In this paper, we present cWatts++, a lightweight virtual power meter that enables accurate power usage measurement in virtualized computing environments such as VMs and containers of Cloud data centers. At the core of cWatts++ is its application-agnostic power model. To this end, we devise two power models (eventModel and raplModel) that are driven by CPU event counters and the Running Average Power Limit (RAPL) feature of modern Intel CPUs, respectively. While eventModel is more generic and, thus, applicable to a wide range of workloads, raplModel is particularly good for CPU-bound workloads. We have evaluated cWatts++ with its two power models in a real system using the PARSEC benchmark suite and our in-house benchmarks. Our evaluation study demonstrates that these power models have an average error of 4.55 and 1.25 percent, respectively, compared with actual power usage measurements of a real power meter, Cabac Power-Mate.
Improved Integral Attack on Reduced-Round Simeck

Simeck, a family of lightweight block ciphers utilizing Simon-like structure, is widely used under resource constrained environment.So far, many cryptanalysis methods have been used to attack Simeck.In this paper, we give the new results of integral cryptanalysis on reduced-round Simeck.First, the exact algebraic degree of Simeck32 is given by parallel computing, and then the 13-round theoretical integral distinguisher is proposed to attack 20-round Simeck32(64).Besides, by using the equivalent-subkey and partial-sum technology, combined with the meet-in-the-middle strategy and subkey relationship, the 22-round Simeck32(64) integral attack is first proposed based on the 15-round integral distinguisher.Furthermore, based on 18-round and 21-round integral distinguishers, the new integral attacks on 26-round Simeck48(96) and 30-round Simeck64(128) are proposed, respectively.These new attacks greatly improve the results of the previous integral attacks for Simeck.
Research on Recommendation System Algorithm Based on Deep Learning Mode in Grid Environment

In order to effectively improve the accuracy of recommending information for users, this paper introduces the deep learning mode by means of grid environment, and proposes a recommendation system algorithm based on deep learning mode in grid environment. Firstly, the advantages and disadvantages of content recommendation and collaborative filtering recommendation algorithms are analyzed. Then, the deep learning mode is introduced, the recommended environment framework is established in the grid environment, and the recommendation algorithm is optimized. Finally, the simulation results show that the proposed recommendation algorithm can quickly establish a user recommendation list and effectively improve the recommendation accuracy.
2014 not found: a cross-platform approach to retrospective web archiving

While web archiving techniques capture snapshots of websites in real time, this article introduces an approach for building special collections for web archiving in retrospect. Retrospective web archiving (RWA) aims to fill-in gaps in existing archives, as well as to expand the boundaries of web archiving from the open web to cross-platform curation, and from national to international perspectives. The proposed approach is tested on a case study of the 2014 War in Gaza. The retrospective collection contains 118,508 unique URIs and relevant metadata, carbon-dated to the period of the military operation, in 46 languages and 5692 domain suffixes, collected from Wikipedia, Google, Twitter and YouTube. Findings suggest that four years after the war, 50% of the URIs were still available on the live web, but only 38% of them have already been archived elsewhere. Although URL sharing on social media was found the most relevant for retrospective curation, the platformisation of the web, along with the popularity of URL shortening services, severely impacts their archival coverage. The article suggests taking into account platform dynamics and cultural differences in link sharing practices, when thinking about future curation policies for both real time and RWA.
HRCR: Hidden Markov-Based Reinforcement to Reduce Churn in Question Answering Forums

The high rate of churning users who abandon the Community Question Answering forums (CQAs) may be one of the crucial issues that hinder their development. More personalized question recommendation to users might help to manage this problem better. In this paper, we propose a new algorithm (we name HRCR) that recommends questions to users such to reduce their churning probability. We present our algorithm in a two-fold structure: First, we use Hidden Markov Models (HMMs) to uncover the users' engagement states inside a CQA. Second, we apply a Reinforcement Learning Model (RL) to recommend users the questions that match better with their engagement mood and thus help them get into a better engagement state (the one with the least churning probability). Experiments on a large-scale offline dataset from Stack Overflow show a meaningful reduction in the churning probability of the users who comply with HRCR's question recommendations.
Guided trilateral filter and its application to ultrasound image despeckling

Speckle reduction has been a hot research topic in ultrasound imaging community. However, designing a method with better despeckling performance and lower algorithm complexity is still an active pursuit. In this paper, we propose a novel local spatial filtering framework named as the guided trilateral filter (GTF) to implement restoration of a noisy image with a well-fitting statistical distribution model. The GTF is derived as the local maximum likelihood estimation from the probabilities of the residuals between the filtered/guided image and the noisy image. The resulting iteration algorithm is essentially a local weighted filtering whose weights come from the guided image, including spatial distance, range discrepancy and statistical distribution. Choosing specific functions for these trilateral weights can direct to some classical local spatial filters, such as classical bilateral filter, robust bilateral filter, joint/cross bilateral filter, speckle reduction bilateral filter, etc. As a despeckling application of the GTF, we embed the Fisher-Tippett distribution model of ultrasound image into the GTF and thereby present the guided trilateral despeckling filter (GTDF). Experimental results on synthetic and real ultrasound images have demonstrated that the GTDF not only can achieve superior performance against state of the art methods, but also has fast and robust convergence and parameter setting insensitivity.
Network Reconnaissance and Vulnerability Excavation of Secure DDS Systems

Data Distribution Service (DDS) is a realtime peer-to-peer protocol that serves as a scalable middleware between distributed networked systems found in many Industrial IoT domains such as automotive, medical, energy, and defense. Since the initial ratification of the standard, specifications have introduced a Security Model and Service Plugin Interface (SPI) architecture, facilitating authenticated encryption and data centric access control while preserving interoperable data exchange. However, as Secure DDS v1.1, the default plugin specifications presently exchanges digitally signed capability lists of both participants in the clear during the crypto handshake for permission attestation; thus breaching confidentiality of the context of the connection. In this work, we present an attacker model that makes use of network reconnaissance afforded by this leaked context in conjunction with formal verification and model checking to arbitrarily reason about the underlying topology and reachability of information flow, enabling targeted attacks such as selective denial of service, adversarial partitioning of the data bus, or vulnerability excavation of vendor implementations.
Scan-Flood Fill(SCAFF): An Efficient Automatic Precise Region Filling Algorithm for Complicated Regions

Recently, instant level labeling for supervised machine learning requires a considerable number of filled masks. In this paper, we propose an efficient automatic region filling algorithm for complicated regions. Distinguishing between adjacent connected regions, the Main Filling Process scans through all pixels and fills all the pixels except boundary ones with either exterior or interior label color. In this way, we succeed in classifying all the pixels inside the region except boundary ones in the given image to form two groups: a background group and a mask group. We then set all exterior label pixels to background color, and interior label pixels to mask color. With this algorithm, we are able to generate output masks precisely and efficiently even for complicated regions as long as boundary pixels are given. Experimental results show that the proposed algorithm can generate precise masks that allow for various machine learning tasks such as supervised training. This algorithm can effectively handle multiple regions, complicated 'holes' and regions whose boundaries touch the image border. By testing the algorithm on both toy and practical images, we show that the performance of Scan-flood Fill(SCAFF) has achieved favorable results.
Automation of Synthesized Optimal Control Problem Solution for Mobile Robot by Genetic Programming

A problem of synthesized control for mobile robot is considered. Initially, a control synthesis problem is solved and a function is found that ensures the stability of the object relative to a point in the state space. The coordinates of the stability points are searched so that when switching form point to point over a given time interval, the robot moves from the initial conditions to the terminal ones without collisions and with the optimal value of the quality criterion. To solve a control synthesis problem a method of complete binary genetic programming is used. To find the coordinates of stability points a particle swarm optimization is applied. Experiments for object with uncertainties in the right parts are given. The sensitivity to different levels of noise for obtaining optimal control by three methods are presented.
Online sequential attack detection for ADS-B data based on hierarchical temporal memory

In the next generation air traffic surveillance, ADS-B is the primary surveillance method to improve situation awareness capabilities. But ADS-B protocol is absent of sufficient security considerations, especially for data integrity and authentication. As a result, attack patterns on ADS-B data are emerging and efficient attack detection strategies are in great demand to enhance data security. To decrease the time delay of detection, enhance accuracy and mitigate the concept drift impacts, the online sequential attack detection strategy based on hierarchical temporal memory are proposed. By applying binary encoding, ADS-B data is transformed into sparse distribution representation with temporal and spatial correlations. The encoded data is push into hierarchical temporal memory and online learning schemes are established for the ADS-B stream data. With the sequential ADS-B data, hierarchical temporal memory is updated and used to generate the deviations between predictions and original values for the corresponding ADS-B data. Designing and applying deviation analysis, sequential analysis and adaptive threshold check, the differences between normal and novelty are magnified and easy to be distinguished. According to experimental analysis, the attack detection strategy is efficient on processing time and accuracy.
Applications of Virtual Reality in Clinical Psychology and Clinical Cognitive Neuroscience–An Introduction

Simulation technology has a long history of adding value in aviation, military training, automotive/aircraft design, and surgical planning. In clinical psychology, Norcross et al. (2013) surveyed 70 therapy experts regarding interventions they predicted to increase in the next decade and virtual reality (VR) was ranked 4th out of 45 options, with other computer-supported methods occupying 4 out of the top 5 positions. The increased popularity of VR in the news, social media, conferences, and from innovative start-ups may give the impression that VR is something new. However, it is important to look back in time and recognize that as early as the 1960’s, Heilig proposed a multisensory immersive experienced called the Sensorama, and Sutherland and Sproull had created a stereoscopic head mounted display (HMD) (Berryman 2012; Srivastava et al. 2014). The term VR was coined more than 30 years ago by Jaron Lanier and commercial games were distributed to the public as early as 1989 by Mattel (in the US, and by PAX in Japan) for its PowerGlove™ and Nintendo’s failed Virtual Boy™ was released in 1995. Clinical VR applications were proposed as early as the mid 1990’s by Lamson, Pugnetti, Rothbaum, Riva, Rizzo, Weiss, and Wiederhold (named in alphabetical order), among others. Moreover, several scientific journals, conferences, and handbooks dedicated to the subject have been reporting scientific findings for decades.
A Qualitative Evaluation of Two Different Law Enforcement Approaches on Dark Net Markets

This paper presents the results of a qualitative study on discussions about two major law enforcement interventions against Dark Net Market (DNM) users extracted from relevant Reddit forums. We assess the impact of Operation Hyperion and Operation Bayonet (combined with the closure of the site Hansa) by analyzing posts and comments made by users of two Reddit forums created for the discussion of Dark Net Markets. The operations are compared in terms of the size of the discussions, the consequences recorded, and the opinions shared by forum users. We find that Operation Bayonet generated a higher number of discussions on Reddit, and from the qualitative analysis of such discussions it appears that this operation also had a greater impact on the DNM ecosystem.
IoT-based Safety Recognition Service for Construction Site

In this paper, we implemented a service that supports the safety situation of the construction site using IoT sensor network that prevents the dangerous situation of workers. We built a sub-1ghz based Zigbee SensorTags and Edge Routers for sensor networks which is suitable for construction sites. Using the gyroscope and accelerometer data of the wearable device, it provides a safety service that detects worker's falls using Deep Learnings. Using Deep Learning networks falls detection performance was achieved above 94%.
Intelligent Tutoring System

With digitization, a rapid growth is seen in educational technology. Different formal and informal learning contents are available on the internet. Intelligent tutoring system provides personalized e-learning to the learners. Different attributes like historical data, real-time data, behavioral, and cognitive are usually used for personalization. Based on the personalization, the intelligent tutoring system aims to provide easy and effective understanding. Recent research highlights the effect of learner's behavior and emotions on effective teaching-learning process. This chapter provides a brief description of the intelligent tutoring system, current developments, instructional techniques, proposed solution, and future recommendations. The emphasis of the study is to provide insights on self-regulated learning.
Finding the Stars in the Fireworks: Deep Understanding of Motion Sensor Fingerprint

With the proliferation of mobile devices and various sensors (e.g., GPS, magnetometer, accelerometers, gyroscopes) equipped, richer services, e.g. location based services, are provided to users. A series of methods have been proposed to protect the users' privacy, especially the trajectory privacy. Hardware fingerprinting has been demonstrated to be a surprising and effective source for identifying/authenticating devices. In this work, we show that a few data samples collected from the motion sensors are enough to uniquely identify the source mobile device, i.e., the raw motion sensor data serves as a fingerprint of the mobile device. Specifically, we first analytically understand the fingerprinting capacity using features extracted from hardware data. To capture the essential device feature automatically, we design a multi-LSTM neural network to fingerprint mobile device sensor in real-life uses, instead of using handcrafted features by existing work. Using data collected over 6 months, for arbitrary user movements, our fingerprinting model achieves 93% F-score given one second data, while the state-of-the-art work achieves 79% F-score. Given ten seconds randomly sampled data, our model can achieve 98.8% accuracy. We also propose a novel generative model to modify the original sensor data and yield anonymized data with little fingerprint information while retain good data utility.
Counteracting the filter bubble in recommender systems: Novelty-aware matrix factorization

The search for unfamiliar experiences and novelty is one of the main drivers behind all human activities, equally important with harm avoidance and reward dependence. A recommender system personalizes suggestions to individuals to support and guide them in their exploration tasks. Personalization mechanisms and recommender systems limit serendipitous encounters by selectively guessing the next item to show to users and potentially leading them into so-called filter bubbles. In the ideal case, these recommendations, except of being accurate, should be also novel. However, up to now most platforms fail to provide both novel and accurate recommendations. For example, a well-known recommendation algorithm, such as matrix factorization (MF), tries to optimize only the accuracy criterion, while disregarding the novelty of recommended items. In order to counteract the filter bubble, we propose two models, denoted as popularity-based and distance-based NMF, that allow to trade-off the MF performance with respect to the criteria of novelty, while only minimally compromising on accuracy. Our experimental results demonstrate that we attain high accuracy by recommending also novel items.
Comparative usability of an augmented reality sandtable and 3D GIS for education

Augmented Reality (AR) sandtables facilitate the shaping of sand to form a surface that is transformed into a digital terrain map which is projected back onto the sand. Although a mature technology, there are still few instances of sandtables being used in surface analysis. Fundamentally there has not been any reported formal assessment of how well sandtables perform in an educational context compared to other conventional learning environments. We compared learning outcomes from using an AR sandtable versus a conventional 3D GIS to convey key concepts in terrain and hydrological analyses via usability and knowledge testing. Overall results from students at a research-intensive New Zealand university reveal a faster task performance and more learning satisfaction when using the sandtable to undertake experimental tasks. Effectiveness and knowledge quiz results revealed no significant difference between the technologies though there was a trend for more accurate answers with 3D GIS tasks. Student learning wise, the sandtable integrated core concepts (especially morphometry) more effectively though both technologies were otherwise similar. We conclude that sandtables have high potential in geospatial teaching, fostering accessible and engaging means of introducing terrain and hydrological concepts, prior to undertaking a more accurate and precise surface analysis with 3D GIS.