Reviewerly/exHarmony huggingface dataset:
Composing tweets to increase retweets

Brands tweet not only to communicate with followers but also to reach large audiences rapidly when the tweets are retweeted by the followers. People however will retweet only if they recognize within a few seconds that the tweet is on an interesting topic. Brands therefore need insights into how to compose tweets to facilitate topic recognition even when they are just scanned. This is the issue that we address in this research. Specifically, drawing on findings in psycholinguistics, we empirically investigate if tweets composed such that they include more topic-related words that are located closer to the start get more retweets. Results from an investigation of sales-promotional tweets by sixty brands in four categories indicate that tweets that are composed as above do get more retweets. We repeat the investigation using tweets on several other topics from a natural experiment that generated pairs of tweets where each pair is on the same topic but each tweet in the pair is composed differently. This investigation reconfirms the findings from the analysis of retweets of sales-promotional tweets. We conclude by presenting an approach for how social media managers can compose tweets based on our findings.
Recommendations for Enhancing the Results of Cyber Effects

Cyber effects <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">11</sup> An “effect” is a direct or indirect objective (intended) outcome of an action. In warfare, the actions are intended to create effects, typically against the functional capabilities of a material target or to the behaviour of individuals. should be considered an important tool in the toolbox of warfare for the commander of a military operation. This paper discusses the key elements required to enhance decision-making in cyber operations. Many different parameters influence the outcome, but only some of them are internally controllable. This paper outlines how to predict the outcome of cyber effects and how to measure that outcome. It gives advice on developing cyber effect capabilities and reflects on how to integrate cyber effects in a mission as lateral support. The authors recommend a set of best practices for enhancing cyber effects in modern warfare.
Instance selection based on boosting for instance-based learners

Instance selection is one of the most important preprocessing steps in many machine learning tasks. Due to huge data size that is common in many current problems, removing redundant, useless, erroneous or noisy instances is a frequent initial step that is performed before other data mining algorithms are applied. Instance selection as part of this data reduction task is a relevant problem in current data mining research. Many instance selection methods hypothesize a certain way of characterizing the most important instances and then implement an algorithm to keep those important instances. The problem with these methods is that their success depends on the data fulfilling the underlying hypothesis. Other methods just add or delete instances considering only their effect on the accuracy of the nearest neighbor rule. In this paper, we present a new method of this second kind that uses boosting to obtain a subset of instances that is able to improve the classification accuracy of the whole dataset with a significant reduction. The instances are incrementally added by selecting those that maximize the accuracy of the subset using the weighting of instances from the construction of ensembles of classifiers and the step-wise addition of new instances. The method is compared using a large set of 205 different datasets with standard methods and shows the best overall performance.
Exploiting Determinism in Lattice-based Signatures

In this paper, we analyze the implementation level fault vulnerabilities of deterministic lattice-based signature schemes. In particular, we extend the practicality of skip-addition fault attacks through exploitation of determinism in Dilithium and qTESLA signature schemes, which are two leading candidates for the NIST standardization of post-quantum cryptography. We show that single targeted faults injected in the signing procedure allow to recover an important portion of the secret key. Though faults injected in the signing procedure do not recover all the secret key elements, we propose a novel forgery algorithm that allows the attacker to sign any given message with only the extracted portion of the secret key. We perform experimental validation of our attack using Electromagnetic fault injection on reference implementations taken from the pqm4 library, a benchmarking and testing framework for post quantum cryptographic implementations for the ARM Cortex-M4 microcontroller. We also show that our attacks break two well known countermeasures known to protect against skip-addition fault attacks. We further propose an efficient mitigation strategy against our attack that exponentially increases the attacker's complexity at almost zero increase in computational complexity.
Blockchain-Based Interoperable Electronic Health Record Sharing Framework

Electronic Health Records have proven to be indispensable yet continue to present a host of problems. One of the most pressing concerns is how to share patient information freely and efficiently. Hospitals and clinics may share data internally, but there is an inability due to the lack of infrastructure or an unwillingness to share data between systems. Implementing a peer-to-peer distributed digital ledger, known as a blockchain, to record and transmit transactional data in conjunction with Cloud based technologies may be a solution to bridge the communication gap. This paper sets forth a new approach for a blockchain and cloud computing network utilizing Amazon Web Services and Ethereum blockchain to facilitate semantic level interoperability of Electronic Health Records systems without standardized data forms and formatting.
Further Analysis of PRNG-Based Key Derivation Functions

The Internet of Things (IoT) is growing at a rapid pace. With everyday applications and services becoming wirelessly networked, security still is a major concern. Many of these sensors and devices have limitations, such as low power consumption, reduced memory storage, and reduced fixed point processing capabilities. Therefore, it is imperative that high-performance security primitives are used to maximize the lifetime of these devices while minimally impacting memory storage and timing requirements. Previous work presented a residue number system (RNS)-based pseudorandom number generator (PRNG)-based key derivation function (KDF) (PKDF) that showed good initial energy-efficient performance for the IoT devices. This paper provides additional analysis on the PRNG-based security and draws a comparison to a current industry-standard KDF. Subsequently, embedded software implementations were performed on an MSP430 and MSP432 and compared with the transport layer security (TLS) 1.3 hash-based message authentication code (HMAC) key derivation function (HKDF); these results demonstrate substantial computational savings for the PKDF approach, while both pass the NIST randomness quality tests. Finally, hardware translation for the PKDF is evaluated through the Mathworks' HDL Coder toolchain and mapping for throughput and die area approximation on an Intel <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">®</sup> Arria 10 FPGA.
Android Malware Identification Based on Traffic Analysis

As numerous new techniques for Android malware attacks have growingly emerged and evolved, Android malware identification is extremely crucial to prevent mobile applications from being hacked. Machine learning techniques have shown extraordinary capabilities in various fields. A common problem with existing research of malware traffic identification based on machine learning approaches is the need to design a set of features that accurately reflect network traffic characteristics. Obtaining a high accuracy for identifying Android malware traffic is also a challenging problem. This paper analyses the Android malware traffic and extract 15 features which is a combination of time-related network flow feature and packets feature. We then use three supervised machine learning methods to identify Android malware traffic. Experimental results show that the feature set we proposed can accurately characterize the traffic and all three classifiers achieve high accuracy.
Multi-Layer Pruning Framework for Compressing Single Shot MultiBox Detector

We propose a framework for compressing state-of-the-art Single Shot MultiBox Detector (SSD). The framework addresses compression in the following stages: Sparsity Induction, Filter Selection, and Filter Pruning. In the Sparsity Induction stage, the object detector model is sparsified via an improved global threshold. In Filter Selection & Pruning stage, we select and remove filters using sparsity statistics of filter weights in two consecutive convolutional layers. This results in the model with the size smaller than most existing compact architectures. We evaluate the performance of our framework with multiple datasets and compare over multiple methods. Experimental results show that our method achieves state-of-the-art compression of 6.7X and 4.9X on PASCAL VOC dataset on models SSD300 and SSD512 respectively. We further show that the method produces maximum compression of 26X with SSD512 on German Traffic Sign Detection Benchmark (GTSDB). Additionally, we also empirically show our method's adaptability for classification based architecture VGG16 on datasets CIFAR and German Traffic Sign Recognition Benchmark (GTSRB) achieving a compression rate of 125X and 200X with the reduction in flops by 90.50% and 96.6% respectively with no loss of accuracy. In addition to this, our method does not require any special libraries or hardware support for the resulting compressed models.
How Prior Programming Experience Affects Students' Pair Programming Experiences and Outcomes

Pair programming is a collaborative learning approach in computer science in which students (or employees) work closely with a partner on the same programming task. A long-standing question within pair programming is whether certain combinations of students lead to greater learning, effort, and/or performance. Earlier studies have explored the role of prior programming experience, including the discrepancy between partners' experience, as a potentially important factor in shaping these outcomes. However, the previous findings are highly inconsistent, which may result from divergent (and often suboptimal) ways of defining previous experience or skill, problems with self-selection into pairs, and small sample sizes that often yield nonsignificant results. The present study sought to improve on all of these limitations through an examination of 587 undergraduates who each participated in three different randomly assigned pairings. Not surprisingly, students' own programming experience was positively related to understanding concepts from lab, confidence in the finished product, and overall interest in computer science. However, students who worked with a more experienced partner actually had poorer outcomes, including lower effort exerted on the assignment, perceptions that their partner gave more effort than they did, less time in the driving role (i.e., typing out the assignment), lower understanding of concepts from lab, and less interest in computer science overall. The partner's experience is unrelated to other outcomes, including confidence in the finished assignment, feeling productive during lab session, and average grades received during the pairing. These results provide important considerations for the assignment of students to pairs.
Optimization of privacy-utility trade-offs under informational self-determination

The pervasiveness of Internet of Things results in vast volumes of personal data generated by smart devices of users (data producers) such as smart phones, wearables and other embedded sensors. It is a common requirement, especially for Big Data analytics systems, to transfer these large in scale and distributed data to centralized computational systems for analysis. Nevertheless, third parties that run and manage these systems (data consumers) do not always guarantee users’ privacy. Their primary interest is to improve utility that is usually a metric related to the performance, costs and the quality of service. There are several techniques that mask user-generated data to ensure privacy, e.g. differential privacy. Setting up a process for masking data, referred to in this paper as a ‘privacy setting’, decreases on the one hand the utility of data analytics, while, on the other hand, increases privacy. This paper studies parameterizations of privacy settings that regulate the trade-off between maximum utility, minimum privacy and minimum utility, maximum privacy, where utility refers to the accuracy in the estimations of aggregation functions. Privacy settings can be universally applied as system-wide parameterizations and policies (homogeneous data sharing). Nonetheless they can also be applied autonomously by each user or decided under the influence of (monetary) incentives (heterogeneous data sharing). This latter diversity in data sharing by informational self-determination plays a key role on the privacy-utility trajectories as shown in this paper both theoretically and empirically. A generic and novel computational framework is introduced for measuring privacy-utility trade-offs and their Pareto optimization. The framework computes a broad spectrum of such trade-offs that form privacy-utility trajectories under homogeneous and heterogeneous data sharing. The practical use of the framework is experimentally evaluated using real-world data from a Smart Grid pilot project in which energy consumers protect their privacy by regulating the quality of the shared power demand data, while utility companies make accurate estimations of the aggregate load in the network to manage the power grid. Over 20,000 differential privacy settings are applied to shape the computational trajectories that in turn provide a vast potential for data consumers and producers to participate in viable participatory data sharing systems.
Distributed Fusion-Based Policy Search for Fast Robot Locomotion Learning

Deep reinforcement learning methods are developed to deal with challenging locomotion control problems in a robotics domain and can achieve significant performance improvement over conventional control methods. One of their appealing advantages is model-free. In other words, agents learn a control policy completely from scratches with raw high-dimensional sensory observations. However, they often suffer from poor sample-efficiency and instability issues, which make them inapplicable to many engineering systems. This paper presents a distributed fusion-based policy search framework to accelerate robot locomotion learning processes through variance reduction and asynchronous exploration approaches. An adaptive fusion-based variance reduction technique is introduced to improve sample-efficiency. A parametric noise is added to neural network weights, which leads to efficient exploration and ensures consistency in actions. Subsequently, the fusion-based policy gradient estimator is extended to a distributed decoupled actor-critic architecture. This allows the central estimator to handle off-policy data from different actors asynchronously, which fully utilizes CPUs and GPUs to maximize data throughput. The aim of this work is to improve sample-efficiency and convergence speed of deep reinforcement learning in robot locomotion tasks. Simulation results are presented to verify the theoretical results, which show that the proposed algorithm achieves and sometimes surpasses the state-of-the-art performance.
Joint Registration and Fusion of an Infrared Camera and Scanning Radar in a Maritime Context

The number of nodes in sensor networks is continually increasing, and maintaining accurate track estimates inside their common surveillance region is a critical necessity. Modern sensor platforms are likely to carry a range of different sensor modalities, all providing data at differing rates, and with varying degrees of uncertainty. These factors complicate the fusion problem as multiple observation models are required, along with a dynamic prediction model. However, the problem is exacerbated when sensors are not registered correctly with respect to each other, i.e., if they are subject to a static or dynamic bias. In this case, measurements from different sensors may correspond to the same target, but do not correlate with each other when in the same Frame of Reference (FoR), which decreases track accuracy. This paper presents a method to jointly estimate the state of multiple targets in a surveillance region, and to correctly register a radar and an Infrared Search and Track (IRST) system onto the same FoR to perform sensor fusion. Previous work using this type of parent-offspring process has been successful when calibrating a pair of cameras, but has never been attempted on a heterogeneous sensor network, or in a maritime environment. This article presents results on both simulated scenarios and a segment of real data that show a significant increase in track quality in comparison to using incorrectly calibrated sensors or single-radar only.
Understanding and comparing scalable Gaussian process regression for big data

As a non-parametric Bayesian model which produces informative predictive distribution, Gaussian process (GP) has been widely used in various fields, like regression, classification and optimization. The cubic complexity of standard GP however leads to poor scalability, which poses challenges in the era of big data. Hence, various scalable GPs have been developed in the literature in order to improve the scalability while retaining desirable prediction accuracy. This paper devotes to investigating the methodological characteristics and performance of representative global and local scalable GPs including sparse approximations and local aggregations from four main perspectives: scalability, capability, controllability and robustness. The numerical experiments on two toy examples and five real-world datasets with up to 250K points offer the following findings. In terms of scalability, most of the scalable GPs own a time complexity that is linear to the training size. In terms of capability, the sparse approximations capture the long-term spatial correlations, the local aggregations capture the local patterns but suffer from over-fitting in some scenarios. In terms of controllability, we could improve the performance of sparse approximations by simply increasing the inducing size. But this is not the case for local aggregations. In terms of robustness, local aggregations are robust to various initializations of hyperparameters due to the local attention mechanism. Finally, we highlight that the proper hybrid of global and local scalable GPs may be a promising way to improve both the model capability and scalability for big data.
Private Polynomial Computation From Lagrange Encoding

Private computation is a generalization of private information retrieval, in which a user is able to compute a function on a distributed dataset without revealing the identity of that function to the servers. In this paper, it is shown that Lagrange encoding, a powerful technique for encoding Reed-Solomon codes, enables private computation in many cases of interest. In particular, we present a scheme that enables private computation of polynomials of any degree on Lagrange encoded data, while being robust to Byzantine and straggling servers, and to servers colluding to attempt to deduce the identities of the functions to be evaluated. Moreover, incorporating ideas from the well-known Shamir secret sharing scheme allows the data itself to be concealed from the servers as well. Our results extend private computation to high degree polynomials and to data-privacy, and reveal a tight connection between private computation and coded computation.
Recognizing Textures with Mobile Cameras for Pedestrian Safety Applications

As smartphone rooted distractions become commonplace, the lack of compelling safety measures has led to a rise in the number of injuries to distracted walkers. Various solutions address this problem by sensing a pedestrian's walking environment. Existing camera-based approaches have been largely limited to obstacle detection and other forms of object detection. Instead, we present TerraFirma, an approach that performs material recognition on the pedestrian's walking surface. We explore, first, how well commercial off-the-shelf smartphone cameras can learn texture to distinguish among paving materials in uncontrolled outdoor urban settings. Second, we aim at identifying when a distracted user is about to enter the street, which can be used to support safety functions such as warning the user to be cautious. To this end, we gather a unique dataset of street/sidewalk imagery from a pedestrian's perspective, that spans major cities like New York, Paris, and London. We demonstrate that modern phone cameras can be enabled to distinguish materials of walking surfaces in urban areas with more than 90 percent accuracy, and accurately identify when pedestrians transition from sidewalk to street.
Crowdsourcing for click fraud detection

Mobile ads are plagued with fraudulent clicks which is a major challenge for the advertising community. Although popular ad networks use many techniques to detect click fraud, they do not protect the client from possible collusion between publishers and ad networks. In addition, ad networks are not able to monitor the user’s activity for click fraud detection once they are redirected to the advertising site after clicking the ad. We propose a new crowdsource-based system called Click Fraud Crowdsourcing (CFC) that collaborates with both advertisers and ad networks in order to protect both parties from any possible click fraudulent acts. The system benefits from both a global view, where it gathers multiple ad requests corresponding to different ad network-publisher-advertiser combinations, and a local view, where it is able to track the users’ engagement in each advertising website. The results demonstrated that our approach offers a lower false positive rate (0.1) when detecting click fraud as opposed to proposed solutions in the literature, while maintaining a high true positive rate (0.9). Furthermore, we propose a new mobile ad charging model that benefits from our system to charge advertisers based on the duration spent in the advertiser’s website.
StyleBlit: Fast Example‐Based Stylization with Local Guidance

Abstract We present StyleBlit—an efficient example‐based style transfer algorithm that can deliver high‐quality stylized renderings in real‐time on a single‐core CPU. Our technique is especially suitable for style transfer applications that use local guidance ‐ descriptive guiding channels containing large spatial variations. Local guidance encourages transfer of content from the source exemplar to the target image in a semantically meaningful way. Typical local guidance includes, e.g., normal values, texture coordinates or a displacement field. Contrary to previous style transfer techniques, our approach does not involve any computationally expensive optimization. We demonstrate that when local guidance is used, optimization‐based techniques converge to solutions that can be well approximated by simple pixel‐level operations. Inspired by this observation, we designed an algorithm that produces results visually similar to, if not better than, the state‐of‐the‐art, and is several orders of magnitude faster. Our approach is suitable for scenarios with low computational budget such as games and mobile applications.