Reviewerly/exHarmony huggingface dataset:
A Human-Centered Systematic Literature Review of the Computational Approaches for Online Sexual Risk Detection

In the era of big data and artificial intelligence, online risk detection has become a popular research topic. From detecting online harassment to the sexual predation of youth, the state-of-the-art in computational risk detection has the potential to protect particularly vulnerable populations from online victimization. Yet, this is a high-risk, high-reward endeavor that requires a systematic and human-centered approach to synthesize disparate bodies of research across different application domains, so that we can identify best practices, potential gaps, and set a strategic research agenda for leveraging these approaches in a way that betters society. Therefore, we conducted a comprehensive literature review to analyze 73 peer-reviewed articles on computational approaches utilizing text or meta-data/multimedia for online sexual risk detection. We identified sexual grooming (75%), sex trafficking (12%), and sexual harassment and/or abuse (12%) as the three types of sexual risk detection present in the extant literature. Furthermore, we found that the majority (93%) of this work has focused on identifying sexual predators after-the-fact, rather than taking more nuanced approaches to identify potential victims and problematic patterns that could be used to prevent victimization before it occurs. Many studies rely on public datasets (82%) and third-party annotators (33%) to establish ground truth and train their algorithms. Finally, the majority of this work (78%) mostly focused on algorithmic performance evaluation of their model and rarely (4%) evaluate these systems with real users. Thus, we urge computational risk detection researchers to integrate more human-centered approaches to both developing and evaluating sexual risk detection algorithms to ensure the broader societal impacts of this important work.
Congestion Attacks in Payment Channel Networks

Payment channel networks provide a fast and scalable solution to relay funds, acting as a second layer to slower and less scalable blockchain protocols. In this paper, we present an accessible, low-cost attack in which the attacker paralyzes multiple payment network channels for several days. The attack is based on overloading channels with requests that are kept unresolved until their expiration time. Reaching the maximum allowed unresolved requests ( $$\mathtt {HTLCs}$$ ) locks the channel for new payments. The attack is in fact inherent to the way off-chain networks are constructed, since limits on the number of unresolved payments are derived from limits on the blockchain. We consider three versions of the attack: one in which the attacker attempts to block as many high liquidity channels as possible, one in which it disconnects as many pairs of nodes as it can, and one in which it tries to isolate individual nodes from the network. We evaluate the costs of these attacks on Bitcoin’s Lightning Network and compare how changes in the network have affected the cost of attack. Specifically, we consider how recent changes to default parameters in each of the main Lightning implementations contribute to the attacks. Finally, we suggest mitigation techniques that make these attacks much harder to carry out.
An Effective Dynamic Path Planning Approach for Mobile Robots Based on Ant Colony Fusion Dynamic Windows

To further improve the path planning of the mobile robot in complex dynamic environments, this paper proposes an enhanced hybrid algorithm by considering the excellent search capability of the ant colony optimization (ACO) for global paths and the advantages of the dynamic window approach (DWA) for local obstacle avoidance. Firstly, we establish a new dynamic environment model based on the motion characteristics of the obstacles. Secondly, we improve the traditional ACO from the pheromone update and heuristic function and then design a strategy to solve the deadlock problem. Considering the actual path requirements of the robot, a new path smoothing method is present. Finally, the robot modeled by DWA obtains navigation information from the global path, and we enhance its trajectory tracking capability and dynamic obstacle avoidance capability by improving the evaluation function. The simulation and experimental results show that our algorithm improves the robot’s navigation capability, search capability, and dynamic obstacle avoidance capability in unknown and complex dynamic environments.
On the Influence of Initial Qubit Placement During NISQ Circuit Compilation

Noisy Intermediate-Scale Quantum (NISQ) machines are not fault-tolerant, operate few qubits (currently, less than hundred), but are capable of executing interesting computations. Above the quantum supremacy threshold (approx. 60 qubits), NISQ machines are expected to be more powerful than existing classical computers. One of the most stringent problems is that computations (expressed as quantum circuits) have to be adapted (compiled) to the NISQ hardware, because the hardware does not support arbitrary interactions between the qubits. This procedure introduces additional gates (e.g. SWAP gates) into the circuits while leaving the implemented computations unchanged. Each additional gate increases the failure rate of the adapted (compiled) circuits, because the hardware and the circuits are not fault-tolerant. It is reasonable to expect that the placement influences the number of additionally introduced gates. Therefore, a combinatorial problem arises: how are circuit qubits allocated (placed) initially to the hardware qubits? The novelty of this work relies on the methodology used to investigate the influence of the initial placement. To this end, we introduce a novel heuristic and cost model to estimate the number of gates necessary to adapt a circuit to a given NISQ architecture. We implement the heuristic (source code available on github) and benchmark it using a standard compiler (e.g. from IBM Qiskit) treated as a black box. Preliminary results indicate that cost reductions of up to 10% can be achieved for practical circuit instances on realistic NISQ architectures only by placing qubits differently than default (trivial placement).
Encoding decisions and expertise in the operator's eyes: Using eye-tracking as input for system adaptation

We investigated the possibility of developing a decision support system (DSS) that integrates eye-fixation measurements to better adapt its suggestions. Indeed, eye fixation give insight into human decision-making: Individuals tend to pay more attention to key information in line with their upcoming selection. Thus, eye-fixation measures can help the DSS to better capture the context that determines user decisions. Twenty-two participants performed a simplified Air Traffic Control (ATC) simulation in which they had to decide to accept or to modify route suggestions according to specific parameter values displayed on the screen. Decisions and fixation times on each parameter were recorded. The user fixation times were used by an algorithm to estimate the utility of each parameter for its decision. Immediately after this training phase, the algorithm generated new route suggestions under two conditions: 1) Taking into account the participant's decisions, 2) Taking into account the participant's decisions plus their visual behavior using the measurements of dwell times on displayed parameters. Results showed that system suggestions were more accurate than the base system when taking into account the participant's decisions, and even more accurate using their dwell times. Capturing the crucial information for the decision using the eye tracker accelerated the DSS learning phase, and thus helped to further enhance the accuracy of consecutive suggestions. Moreover, exploratory eye-tracking analysis reflected two different stages of the decision-making process, with longer dwell times on relevant parameters (i.e. involved in a rule) during the entire decision time course, and frequency of fixations on these relevant parameters that increased, especially during the last fixations prior to the decision. Consequently, future DSS integrating eye-tracking data should pay specific care to the final fixations prior to the decision. In general, our results emphasize the potential interest of eye-tracking to enhance and accelerate system adaptation to user preference, knowledge, and expertise.
Zero-shot Learning of 3D Point Cloud Objects

Recent deep learning architectures can recognize instances of 3D point cloud objects of previously seen classes quite well. At the same time, current 3D depth camera technology allows generating/segmenting a large amount of 3D point cloud objects from an arbitrary scene, for which there is no previously seen training data. A challenge for a 3D point cloud recognition system is, then, to classify objects from new, unseen, classes. This issue can be resolved by adopting a zero-shot learning (ZSL) approach for 3D data, similar to the 2D image version of the same problem. ZSL attempts to classify unseen objects by comparing semantic information (attribute/word vector) of seen and unseen classes. Here, we adapt several recent 3D point cloud recognition systems to the ZSL setting with some changes to their architectures. To the best of our knowledge, this is the first attempt to classify unseen 3D point cloud objects in the ZSL setting. A standard protocol (which includes the choice of datasets and the seen/unseen split) to evaluate such systems is also proposed. Baseline performances are reported using the new protocol on the investigated models. This investigation throws a new challenge to the 3D point cloud recognition community that may instigate numerous future works.
GA-ORB: A New Efficient Feature Extraction Algorithm for Multispectral Images Based on Geometric Algebra

Feature extraction, including feature detection and description, plays an important role in many computer vision applications. The feature extraction for multispectral images is especially challenging because the information is contained in both spectral and spatial spaces. Previous works, such as scale invariant feature transform (SIFT) algorithm based on geometric algebra (GA) theory (GA-SIFT) and speeded up robust features (SURF) algorithm based on GA theory (GA-SURF), have tried to extract features for multispectral images based on GA to simultaneously capture relations among multiple channels. However, those methods are hard to be applied in real-time tasks. Fortunately, oriented fast and rotated brief (ORB) is a feature extraction method faster than SIFT and SURF. But, the existing ORB algorithms are not capable of detecting features for multispectral images directly. In this paper, we propose a novel feature extraction method, geometric algebra based oriented fast and rotated brief (GA-ORB), for multispectral images based on the theory GA. First, the scale information in both spectral and spatial spaces of multispectral images is obtained in GA, where the inherent spectral structures can be retained successfully. Then, referring to the ORB, the images are computed in different scales and the interest points are detected and described in the GA space. The experimental results show that the GA-ORB outperforms some previous algorithms with respect to distinctiveness and robustness in extracting and matching interest points, and it can be computed much faster.
Discovering Stable Periodic-Frequent Patterns in Transactional Data

Periodic-frequent patterns are sets of items (values) that periodically appear in a sequence of transactions. The periodicity of a pattern is measured by counting the number of times that its periods (the interval between two successive occurrences of the patterns) are greater than a user-defined maxPer threshold. However, an important limitation of this model is that it can find many patterns having a periodicity that vary widely due to the strict maxPer constraint. But finding stable patterns is desirable for many applications as they are more predictable than unstable patterns. This paper addresses this limitation by proposing to discover a novel type of periodic-frequent patterns in transactional databases, called Stable Periodic-frequent Pattern (SPP), which are patterns having a stable periodicity, and a pattern-growth algorithm named SPP-growth to discover all SPP. An experimental evaluation on four datasets shows that SPP-growth is efficient and can find insightful patterns that are not found by traditional algorithms.
Collecting, Monitoring, and Analyzing Facility and Systems Data at the National Energy Research Scientific Computing Center

As high-performance computing (HPC) resources continue to grow in size and complexity, so too does the volume and velocity of the operational data that is associated with them. At such scales, new mechanisms and technologies are required to continuously gather, store, and analyze this data in near-real time from heterogeneous and distributed sources without impacting the underlying data center operations or HPC resource utilization. In this paper, we describe our experiences in designing and implementing an infrastructure for extreme-scale operational data collection, known as the Operations Monitoring and Notification Infrastructure (OMNI) at the National Energy Research Scientific Computing (NERSC) center at Lawrence Berkeley National Laboratory. OMNI currently holds over 522 billion records of online operational data (totaling over 125TB) and can ingest new data points at an average rate of 25,000 data points per second. Using OMNI as a central repository, facilities and environmental data can be seamlessly integrated and correlated with machine metrics, job scheduler information, network errors, and more, providing a holistic view of data center operations. To demonstrate the value of real-time operational data collection, we present a number of real-world case studies for which having OMNI data readily available led to key operational insights at NERSC. The case results include a reduction in the downtime of an HPC system during a facility transition, as well as a $2.5 million electrical substation savings for the next-generation Perlmutter HPC system.
IoT device fingerprinting for relieving pressure in the access control

As mass devices access to network in the era of the Internet of Things (IoT), the network access control becomes more important. Traditional authentication mechanisms are no longer suitable for IoT devices with limited physical and computational resources because of their complexity. This problem can be solved by the device fingerprinting, which can identify devices only by their traffic characteristics. However, existing device fingerprinting technologies cannot accurately identify devices' type from the same manufacturer which equips these devices with similar hardwares and softwares. Therefore, we proposed the TSMC-SVM algorithm which introduced the cosine similarity into the SVM for improving the identification accuracy of these similar devices described above. Experiments illustrate that the average identification accuracy of the TSMC-SVM reaches 93.2%.
Deep Neural Networks for Query Expansion Using Word Embeddings

Query expansion is a method for alleviating the vocabulary mismatch problem present in information retrieval tasks. Previous works have shown that terms selected for query expansion by traditional pseudo-relevance feedback methods such as mixture model are not always helpful to the retrieval process. In this paper, we show that this is also true for more recently proposed embedding-based query expansion methods. We then introduce an artificial neural network classifier, which uses term word embeddings as input, to predict the usefulness of query expansion terms. Experiments on four TREC newswire and web collections show that using terms selected by the classifier for expansion significantly improves retrieval performance compared to competitive baselines. The results are also shown to be more robust than the baselines.
Improving Robustness of Neural Machine Translation with Multi-task Learning

While neural machine translation (NMT) achieves remarkable performance on clean, in-domain text, performance is known to degrade drastically when facing text which is full of typos, grammatical errors and other varieties of noise. In this work, we propose a multi-task learning algorithm for transformer-based MT systems that is more resilient to this noise. We describe our submission to the WMT 2019 Robustness shared task based on this method. Our model achieves a BLEU score of 32.8 on the shared task French to English dataset, which is 7.1 BLEU points higher than the baseline vanilla transformer trained with clean text.
The reservoir’s perspective on generalized synchronization

We employ reservoir computing for a reconstruction task in coupled chaotic systems, across a range of dynamical relationships including generalized synchronization. For a drive-response setup, a temporal representation of the synchronized state is discussed as an alternative to the known instantaneous form. The reservoir has access to both representations through its fading memory property, each with advantages in different dynamical regimes. We also extract signatures of the maximal conditional Lyapunov exponent in the performance of variations of the reservoir topology. Moreover, the reservoir model reproduces different levels of consistency where there is no synchronization. In a bidirectional coupling setup, high reconstruction accuracy is achieved despite poor observability and independent of generalized synchronization.
New Empirical Traceability Analysis of CryptoNote-Style Blockchains

The cascade effect attacks (PETS’ 18) on the untraceability of Monero are circumvented by two approaches. The first one is to increase the minimum ring size of each input, from 3 (version 0.9.0) to 7 in the latest update (version 0.12.0). The second approach is introducing the ring confidential transactions with enhanced privacy guarantee. However, so far, no formal analysis has been conducted on the level of anonymity provided by the new countermeasures in Monero. In addition, since Monero is only an example of leading CryptoNote-style blockchains, the actual privacy guarantee provided by other similar blockchains in the wild remains unknown. In this paper, we propose a more sophisticated statistical analysis on CryptoNote-style cryptocurrencies. In particular, we introduce a new attack on the transaction untraceability called closed set attack. We prove that our attack is optimal assuming that no additional information is given. In other words, in terms of the result, $$\textit{closed set}$$ attack is equivalent to brute-force attack, which exhausts all possible input choices and removes those that are impossible given the constraints imposed by the mixins of each transaction. To verify the impact of our attack in reality, we conduct experiments on the top 3 CryptoNote-style cryptocurrencies, namely, Monero, Bytecoin and DigitalNote, according to their market capitalization. Since the computational cost of performing $$\textit{closed set}$$ attack is prohibitively expensive, we propose an efficient algorithm, called clustering algorithm, to (approximately) implement our attack. By combining our clustering method with the cascade attack, we are able to identify the real coin being spent in $$70.52\%$$ Monero inputs, $$74.25\%$$ Bytecoin inputs, and in $$91.56\%$$ DigitalNote inputs. In addition, we provide a theoretical analysis on the identified $$\textit{closed set}$$ attack, i.e., if every input in a CryptoNote-style blockchain has 3 mixins, and all mixins are sampled uniformly from all existing coins, the success rate of this attack is very small (about $$2^{-19}$$ ). Given that $$\textit{closed set}$$ attack is equivalent to the best possible statistical attack, our findings provide two key insights. First, the current system configuration of Monero is secure against statistical attacks, as the minimum number of mixin is 6. Second, we identify a new factor in improving anonymity, that is, the number of unspent keys. Our analysis indicates that the number of mixins in an input does not need to be very large, if the percentage of unspent keys is high.
Binders by day, labels by night: effect instances via lexically scoped handlers

Handlers of algebraic effects aspire to be a practical and robust programming construct that allows one to define, use, and combine different computational effects. Interestingly, a critical problem that still bars the way to their popular adoption is how to combine different uses of the same effect in a program, particularly in a language with a static type-and-effect system. For example, it is rudimentary to define the “mutable memory cell” effect as a pair of operations, put and get , together with a handler, but it is far from obvious how to use this effect a number of times to operate a number of memory cells in a single context. In this paper, we propose a solution based on lexically scoped effects in which each use (an “instance”) of an effect can be singled out by name, bound by an enclosing handler and tracked in the type of the expression. Such a setting proves to be delicate with respect to the choice of semantics, as it depends on the explosive mixture of effects, polymorphism, and reduction under binders. Hence, we devise a novel approach to Kripke-style logical relations that can deal with open terms, which allows us to prove the desired properties of our calculus. We formalise our core results in Coq, and introduce an experimental surface-level programming language to show that our approach is applicable in practice.
SynthText3D: synthesizing scene text images from 3D virtual worlds

With the development of deep neural networks, the demand for a significant amount of annotated training data becomes the performance bottlenecks in many fields of research and applications. Image synthesis can generate annotated images automatically and freely, which gains increasing attention recently. In this paper, we propose to synthesize scene text images from the 3D virtual worlds, where the precise descriptions of scenes, editable illumination/visibility, and realistic physics are provided. Different from the previous methods which paste the rendered text on static 2D images, our method can render the 3D virtual scene and text instances as an entirety. In this way, real-world variations, including complex perspective transformations, various illuminations, and occlusions, can be realized in our synthesized scene text images. Moreover, the same text instances with various viewpoints can be produced by randomly moving and rotating the virtual camera, which acts as human eyes. The experiments on the standard scene text detection benchmarks using the generated synthetic data demonstrate the effectiveness and superiority of the proposed method.
Constraint handling technique for four-bar linkage path generation using self-adaptive teaching–learning-based optimization with a diversity archive

This article proposes an alternative constraint handling technique for the four-bar linkage path generation problem. The constraint handling technique that is traditionally applied uses an exterior penalty function, and has been found to be inefficient, particularly when dealing with constraints on the input angles. The new technique deals with both input crank rotation and Grashof's criterion. Four classical path generation problems are used to test the performance of the proposed technique. A new adaptive teaching–learning-based optimization (TLBO) scheme is used to solve several optimization problems. This technique is referred to here as teaching–learning-based optimization with a diversity archive (ATLBO-DA), and was specifically developed for this design problem. The results show that this new design concept gives better results than those of previous work, and that ATLBO-DA is superior to the original version and other metaheuristic algorithms.