Reviewerly/exHarmony huggingface dataset:
Vocalising Violence

Public services are being increasingly scrutinised for their ability to be responsive and adaptive to their service users' needs. For service delivery in domestic violence, many aspire to include feedback from service users on their practice, to drive change in their organisation and performance. Current approaches for capturing and using feedback (i.e. surveys) often fail to record rich, in-depth perspectives that audio-video media affords. In this paper, we present the novel application of a voice-based technology to capture and use feedback to reflect on the delivery of a domestic violence intervention. Across four months, we undertook ethnographic fieldwork through observations of four deployments and four reflective discussions with service-staff in their delivery of a novel domestic violence prevention intervention for violent men. Our findings highlight the tensions with how voice can act as a resource to reflect on and refine existing service practices, and offers insights into how technology can play a more practical role in wider service design.
Optimization Based Multi-View Coverage Path Planning for Autonomous Structure From Motion Recordings

The area of autonomous mapping and autonomous maintenance using unmanned robots has received much attention in recent years. However, usually only single-view coverage is ensured, which is not sufficient if a camera-based three-dimensional reconstruction is desired. If high precision is required for this reconstruction, computations are expensive and not real-time feasible, such that recording and reconstruction procedure must be executed consecutively. This calls for a reliable method for collecting a sufficient set of images during the recording procedure. In this letter, we present the first approach for multi-view coverage path planning, specifically targeted at an autonomous structure from motion data acquisition. For that purpose, we introduce an incremental, computational efficient update formula, that gives an upper bound on the expected reconstruction quality of observed points on the boundary of a structure. Using this quality estimate, we iteratively solve the coverage path-planning problem by greedily selecting a next-best-view as a solution to an optimal experimental design problem. Additional geometric and dynamic constraints ensure the admissibility of the solution. The correctness of our quality estimate and coverage path-planning approach are validated in the physics simulator Gazebo.
Crime Prediction Using Data Mining and Machine Learning

In order to predict the crime in YD county, data mining and machine learning are used in this paper. The aim of the study is to show the pattern and rate of crime in YD county based on the data collected and to show the relationships that exist among the various crime types and crime Variable. Analyzing this data set can provide insight on crime activities within YD county. By introducing formula and methods of Bayesian network, random tree and neural network in machine learning and big data, to analyze the crime rules from the collected data. According to the statistics released by the YD county From 2012-09-01 to 2015-07-21, The crime of smuggling, selling, transporting and manufacturing drugs, Theft, Intentional injury, Illegal business crime, Illegal possession of drugs, Rape, Crime of fraud, Gang fighting, manslaughter, Robbery made the top ten list of crime types with high number of crimes. The crime rate of drugs was the highest, reaching 46.86%, farmers are the majority, accounting for 97.07%, people under the age of 35 are the subject of crime. Males accounted for 90.17% of crimes committed, while females accounted for 9.83%. For ethnic groups, the top five were han, yi, wa, dai and lang, accounting for 68.43%, 23.43%, 1.88%, 1.67% and 1.25% respectively. By adopting random forest, Bayesian networks, and neural network methods, we obtained the decision rules for criminal variables. By comparison, the classification effect of Random Trees is better than that of Neural Networks and Bayesian Networks. Through the data collection of the three algorithms, the validity and accuracy of the random tree algorithm in predicting crime data are observed. The performance of the Bayesian network algorithm is relatively poor, probably due to the existence of certain random factors in various crimes and related features (the correlation between the three algorithms is low).
A Novel Tagging Augmented LDA Model for Clustering

Clustering has become an increasingly important task in the analysis of large documents. Clustering aims to organize these documents, and facilitate better search and knowledge extraction. Most existing clustering methods that use user-generated tags only consider their positive influence for improving automatic clustering performance. The authors argue that not all user-generated tags can provide useful information for clustering. In this article, the authors propose a new solution for clustering, named HRT-LDA (High Representation Tags Latent Dirichlet Allocation), which considers the effects of different tags on clustering performance. For this, the authors perform a tag filtering strategy and a tag appending strategy based on transfer learning, Word2vec, TF-IDF and semantic computing. Extensive experiments on real-world datasets demonstrate that HRT-LDA outperforms the state-of-the-art tagging augmented LDA methods for clustering.
Enforcing Fairness in Blockchain Transaction Ordering

In Blockchain networks involving multiple applications, the quality of service of an application is affected by the transaction ordering. We study a setting where each application is represented by a node, which might attempt to prioritize its own transactions through including them early in blocks added to the blockchain. A fair block proposal of a node follows a random selection of the transactions among the set of pending transactions the node is aware of. On the contrary, a dishonest node includes more of its transactions at the expense of transactions of other applications. In this work, we propose a toolbox of techniques to enforce such a fair block selection. First, we design an accurate statistical testing for the honesty of a proposal and explain it. Next, we describe a reputation system, documenting honesty of nodes to encourage fairness. Our last technique enforces fair block selection through concise commitments on the set of pending transactions known to a node.
Multi-Task Learning with Knowledge Transfer for Facial Attribute Classification

Having achieved satisfying performance in multiple areas, multi-task learning (MTL) is being applied on facial attribute classification. However, most multi-task learning algorithms neglect the latent connections among facial attributes, sorting attributes based on local information only, or merely viewing each attribute as independent. The concept of curriculum learning suggests that in multi-task learning, "easy" tasks can be learned first and used to guide the learning process of "hard" tasks. Inspired, we propose KT-MTL, a novel MTL network with knowledge transfer for facial attribute classification. Depending only on label information, attributes are divided into multiple tasks by spectral clustering and labeled as "strong" or "weak" embodying their correlation extent. During training, parameters learned in "strong" network are transferred to "weak" net, imitating the teacher-student learning process. Both parts contribute to the total loss with a specifically designed loss function. The proposed network archives a competitive overall accuracy score of above 92% on aligned CelebA images and the highest accuracy of 91.89% on "weak" tasks.
Diversity and Novelty in Social-Based Collaborative Filtering

Social-based recommenders seek to exploit the mechanisms of homophily and influence observed in social networks in order to provide more accurate recommendations. The way they achieve this is by enforcing similar preferences among users that are socially connected. It is thus reasonable to question whether such approaches lead to the formation of echo chambers, i.e., social groups with a narrow set of preferences and which receive recommendations with low diversity and novelty. This work studies this research question and quantifies the diversity and novelty of existing methods. An important finding is that it is possible to increase accuracy without sacrificing diversity and novelty.
$\eta$ _CODE: A Differential Evolution With $\eta$ _Cauchy Operator for Global Numerical Optimization

Differential evolution (DE) algorithm is a global optimization algorithm over continuous search space. DE also has been applied in many fields, such as artificial neural networks, chemical engineering, mechanical design, robotics, signal processing, biological information, and economics. At the same time, as a powerful evolutionary algorithm for solving global numerical optimization problems, the DE algorithm has drawn more and more attention. However, how to make a proper balance between the global and local search is a burning question and to limit the optimization performance of DE. In this paper, an improved algorithm η_CODE with a new η_Cauchy operator is proposed to enhance the global and local search ability of a well-known DE variant JADE. In order to guarantee the effective performance of the proposed operator, all the fitness values are ranked through a ranking scheme based on increasing order before a new η_Cauchy operator is conducted. The pNP individuals that have better fitness are selected and carried out Cauchy disturbance operation considering the complexity of the algorithm. The Dynamic parameter mechanism is utilized to select pNP individuals that number is also adjusted dynamically in each generation. The scale factor F and crossover probability CR are obtained with Lehmer mean without using determined parameter c in JADE, which aims to balance the exploration and exploitation of the algorithm during the running time. A total of sixty benchmark functions from CEC2014 and CEC2017 on real parameter optimization are employed to prove the validity of η_CODE for solving complex high-dimensional problems. The experiments indicate that η_CODE is better than or at least comparable with several state-of-the-art DE variants, including JADE, SinDE, TSDE, AGDE, and EFADE in the global numerical optimization problems. In order to further analyze the performance of η_CODE, we also select extra two high-powered modified algorithms called EBLSHADE and LSHADESPACMA based on LSHADE to discuss advantages and disadvantages of the proposed algorithm.
What 30 Days Tells Us About 3 Years

The ubiquity of wearable fitness trackers offers extensive opportunities for research on personal health. However, barriers specific to these trackers such as device abandonment and non-adherence often lead to substantial losses in data. As such, further research into adherence behaviors may derive the insights necessary to address these challenges and lead to more effective long-term studies. This paper serves to explore this approach: investigating the adherence behaviors of 617 college students belonging to a three-year observational study in which participants were monitored via Fitbit Charge HRs. Using this data, our objective was to assess the association between early adherence behaviors and device abandonment/long-term adherence. Adherence behavior from as early as participants' first 10 days in the study correlated with device abandonment and adherence over the next three years. Participants with unsatisfactory adherence in their first 30 days were twice as likely to abandon their devices and were, on average, 11% less adherent each month. The findings in this paper identify the stability of adherence behaviors, feasibility of their early detection and motivate the need to address non-adherent study participants early. Throughout these results, we discuss how the insights gathered from this work may shape the design of future long-term studies to minimize user attrition and promote prolonged engagement.
Promises and Pitfalls: The Two-Faced Nature of Streaming and Social Media Platforms for Beirut-Based Independent Musicians

I argue that streaming services like SoundCloud and Spotify are key elements of a two-faced virtual ecosystem – including social media and crowd-funding platforms – that independent musicians worldwide must navigate to achieve economic viability of their music. The two-facedness of these technologies lies in their promotion as democratic and open gateways for musicians at the same time as they actually serve as gatekeepers that throw up hidden walls along lines including national origin and language. I begin by introducing the music scenes of Beirut, Lebanon, where I have done ethnographic research since 2015. I then lay out the importance of online modes of sociality for these musicians and their fans, focusing on social media and streaming services. SoundCloud, in particular, illustrates the two-faced nature of these technologies. Throughout, I offer case studies of how several musicians and other culture workers are navigating the promise and constraints of these technologies.
Yelling at Your TV

Millions of consumers issue voice queries through television-based entertainment systems such as the Comcast X1, the Amazon Fire TV, and Roku TV. Automatic speech recognition (ASR) systems are responsible for transcribing these voice queries into text to feed downstream natural language understanding modules. However, ASR is far from perfect, often producing incorrect transcriptions and forcing users to take corrective action. To better understand their impact on sessions, this paper characterizes speech recognition errors as well as subsequent user responses. We provide both quantitative and qualitative analyses, examining the acoustic as well as lexical attributes of the utterances. This work represents, to our knowledge, the first analysis of speech recognition errors from real users on a widely-deployed entertainment system.
Visual Quality Assessment for Motion Compensated Frame Interpolation

Current benchmarks for optical flow algorithms evaluate the estimation quality by comparing their predicted flow field with the ground truth, and additionally may compare interpolated frames, based on these predictions, with the correct frames from the actual image sequences. For the latter comparisons, objective measures such as mean square errors are applied. However, for applications like image interpolation, the expected user's quality of experience cannot be fully deduced from such simple quality measures. Therefore, we conducted a subjective quality assessment study by crowdsourcing for the interpolated images provided in one of the optical flow benchmarks, the Middlebury benchmark. We used paired comparisons with forced choice and reconstructed absolute quality scale values according to Thurstone's model using the classical least squares method. The results give rise to a re-ranking of 141 participating algorithms w.r.t. visual quality of interpolated frames mostly based on optical flow estimation. Our re-ranking result shows the necessity of visual quality assessment as another evaluation metric for optical flow and frame interpolation benchmarks.
Implicit entity linking in tweets: An ad-hoc retrieval approach

Within the context of Twitter analytics, the notion of implicit entity linking has recently been introduced to refer to the identification of a named entity, which is central to the topic of the tweet, but whose surface form is not present in the tweet itself. Compared to traditional forms of entit y linking where the linking process revolves around an identified surface form of a potential entity, implicit entity linking relies on contextual clues to determine whether an implicit entity is present within a given tweet and if so, which entity is being referenced. The objective of this paper, while introducing and publicly sharing a comprehensive gold standard dataset for implicit entity linking, is to perform the task of implicit entity linking. The dataset consists of 7,870 tweets, which are classified as either containing implicit entities, explicit entities, both, or neither. The implicit entities are then linked to three levels of entities on Wikipedia, namely coarse-grained level, e.g., Person, Fine-grained level, e.g., Comedian, and the actual entity, e.g., Seinfeld. The proposed model in this work formulates the problem of implicit entity linking as an ad-hoc document retrieval process where the input query is the tweet, which needs to be implicitly linked and the document space is the set of textual descriptions of entities in the knowledge base. The novel contributions of our work include: 1) designing and collecting a gold standard dataset for the task of implicit entity linking; 2) defining the implicit entity linking process as an ad-hoc document retrieval task; and 3) proposing a neural embedding-based feature function that is interpolated with prior term dependency and entity-based feature functions to enhance implicit entity linking. We systematically compare our work with existing work in this area and show that our method is able to provide improvements on a number of retrieval measures.
Introspection as an action in relational models

This work studies positive and negative introspection not as 'static' properties an agent might or might not have, but rather as epistemic actions that change the agent's knowledge. The proposed actions include not only operations for achieving full introspection (first with respect to all formulas, and then with respect to a particular χ), but also acts for increasing the agent's introspection by only one degree. In all cases, the actions are represented as model update operations, with matching modalities for expressing the operations' effects. Sound and complete axiom systems are provided in most cases, and some properties of the operations are explored.
Self-Sovereign Identity for IoT Devices

This work is an overview of different approaches to the self-sovereign identity (SSI) concept. The idea of constructing of a digital passport for each and every person in the world is not unique, but with the growing interest and progress of distributed ledgers, a new way of dealing with existing problems appeared. On the other hand, in our point of view, a lot of development groups are working in parallel on the similar topics, yet it is not clear what is going on inside. In this paper we will try to define the differences and discuss both pros and cons of using such commonly known technologies as Sovrin based upon the Hyperledger Indy technology, Civic, Jolocom, uPort and some others. Besides, we’ll tackle the idea of using the SSI for inanimate object and how it can be constructed in this way.
Road Intersection Moving Object Detection by 360-degree View Camera

This paper presents a new approach in detecting moving objects at road intersections by using 360-degree view camera THETA. The camera is installed at a corner of the intersection. We have experimentally found that images from single THETA can capture the all directions of a road intersection. Furthermore, we propose a new method to detect moving objects from THETA images. Through the real experiments, we could confirm that our methods can detect moving objects such as pedestrians, bicycles and wheelchairs effectively, with a small false positives rate.
Efficient Secure Multi-Party Protocols for Decision Tree Classification

We propose novel secure multi-party protocols for decision-tree classification. Our protocols hide not only an input vector and an output class but also the structure of the tree, which incurs an exponential communication complexity in terms of the maximum depth of the tree, $$d_{max}$$ , for a naive construction. We tackle this problem by applying Oblivious RAM (ORAM) and obtain two efficient constructions with polynomial communication complexity (that counts the number of multiplications). The first protocol simulates ORAM in secure multi-party computation. The communication complexity of the first protocol is $$O(d_{max}^3 \log d_{max})$$ in the online phase and $$O(d_{max}^4 \log d_{max})$$ in total. We then improve this protocol by removing the position-map accesses, which is the most time-consuming parts in the ORAM. In the second protocol, we reduce the communication complexity to $$O(d_{max}^2 \log d_{max})$$ in the online phase and $$O(d_{max}^3 \log d_{max})$$ in total, and also reduce the number of rounds from $$O(d_{max}^2)$$ to $$O(d_{max})$$ . We implemented the proposed two constructions and the naive one, and experimentally evaluated their performance.
Multi-agent path finding on real robots

The problem of Multi-Agent Path Finding (MAPF) is to find paths for a fixed set of agents from their current locations to some desired locations in such a way that the agents do not collide with each other. This problem has been extensively theoretically studied, frequently using an abstract model, that expects uniform durations of moving primitives and perfect synchronization of agents/robots. In this paper we study the question of how the abstract plans generated by existing MAPF algorithms perform in practice when executed on real robots, namely Ozobots. In particular, we use several abstract models of MAPF, including a robust version and a version that assumes turning of a robot, we translate the abstract plans to sequences of motion primitives executable on Ozobots, and we empirically compare the quality of plan execution (real makespan, the number of collisions).
Fast Multi-Criteria Service Selection for Multi-User Composite Applications

As cloud computing becomes the prevailing aspect of software engineering, paradigms such as Service-Based Systems (SBSs) or Software as a Service (SaaS) are coming into focus. They are based on cloud services responding to numerous client requests. Selecting the actual service instance for request can be an issue, if requirements for multiple Quality of Service (QoS) attributes need to be satisfied for many users simultaneously. The problem becomes more complex if we take into account the compositeness of users applications, consisting of many tasks, where QoS properties are calculated over the whole composition. The existing approaches for this problem lack either efficiency or generality. In this paper, we propose a fast heuristic method for multi-criteria service selection, designed for multi-user composite workflows with the goal of satisfying all, or as many as possible, of the given QoS requirements. The proposed method reduces the problem to several independent transportation problems, using a global-aware utility cost based on expected compositional QoS, and iterative solution improvements. Apart from being more general than the existing approaches, the proposed method turns out to be more efficient than the alternatives (up to 5x faster), as shown by extensive experiments covering both special and more general cases.
Fitness comparison by statistical testing in construction of SAT-based guess-and-determine cryptographic attacks

Algebraic cryptanalysis studies breaking ciphers by solving algebraic equations. Some of the promising approaches use SAT solvers for this purpose. Although the corresponding satisfiability problems are hard, their difficulty can often be lowered by choosing a set of variables to brute force over, and by solving each of the corresponding reduced problems using a SAT solver, which is called the guess-and-determine attack. In many successful cipher breaking attempts this set was chosen analytically, however, the nature of the problem makes evolutionary computation a good choice.