Reviewerly/exHarmony huggingface dataset:
Transfer learning for hate speech detection in social media

Abstract Today, the internet is an integral part of our daily lives, enabling people to be more connected than ever before. However, this greater connectivity and access to information increase exposure to harmful content, such as cyber-bullying and cyber-hatred. Models based on machine learning and natural language offer a way to make online platforms safer by identifying hate speech in web text autonomously. However, the main difficulty is annotating a sufficiently large number of examples to train these models. This paper uses a transfer learning technique to leverage two independent datasets jointly and builds a single representation of hate speech. We build an interpretable two-dimensional visualization tool of the constructed hate speech representation—dubbed the Map of Hate—in which multiple datasets can be projected and comparatively analyzed. The hateful content is annotated differently across the two datasets (racist and sexist in one dataset, hateful and offensive in another). However, the common representation successfully projects the harmless class of both datasets into the same space and can be used to uncover labeling errors (false positives). We also show that the joint representation boosts prediction performances when only a limited amount of supervision is available. These methods and insights hold the potential for safer social media and reduce the need to expose human moderators and annotators to distressing online messaging.
Workout Type Recognition and Repetition Counting with CNNs from 3D Acceleration Sensed on the Chest

Sports and workout activities have become important parts of modern life. Nowadays, many people track characteristics about their sport activities with their mobile devices, which feature inertial measurement unit (IMU) sensors. In this paper we present a methodology to detect and recognize workout, as well as to count repetitions done in a recognized type of workout, from a single 3D accelerometer worn at the chest. We consider four different types of workout (pushups, situps, squats and jumping jacks). Our technical approach to workout type recognition and repetition counting is based on machine learning with a convolutional neural network. Our evaluation utilizes data of 10 subjects, which wear a Movesense sensors on their chest during their workout. We thereby find that workouts are recognized correctly on average 89.9% of the time, and the workout repetition counting yields an average detection accuracy of 97.9% over all types of workout.
Co-weighting semantic convolutional features for object retrieval

Deep feature aggregation, which refers to aggregating a set of local convolutional features into a global image-level vector, has attracted increasing attention in object instance retrieval. In this manuscript, we propose an unsupervised framework that aggregates feature maps by an adaptive selection and two weighting strategies. Particularly, the selection process finds the foreground contour by explaining the semantic structure implicated in the feature maps, while two weighting process including an adaptive Gaussian filter that highlights semantic features and an element-value sensitive channel vector that activates feature channels corresponding to sparse yet distinctive image patterns. Experimental results on benchmark image retrieval datasets validate that the selection and two weighting schemes are complementary in improving the discriminative power of image vectors. With the same experimental settings, the proposed approach outperforms state-of-the-art aggregation approaches by a considerable margin.
How the Arrangement of Content and Location Impact the Use of Multiple Distributed Public Displays

Novel social, civic or entertainment opportunities might emerge when spatially distributed public displays become interlinked in meaningful ways. Yet little is still known about the effect of intrinsic design dimensions, such as how multiple displays should be spatially arranged, how their content should be linked, and how their locations and content should dynamically change over time. We therefore conducted a two-month long design study of a distributed public display system that invited passers-by to answer hyperlocal questions. By comparing the performance of different content and location arrangement strategies, we reveal distinct spatiotemporal user engagement patterns, and the specific local conditions that shaped them. We also discovered several contextual factors that inhibit more widescale engagement, among which the conceptual novelty, the apparent purpose, and the perceived cumulative effort to engage with several displays. Consequently, this study provides insights on how public displays can be linked to augment the effects of distribution.
Event Recommendation using Social Media

Every day, lots of events keep happening around the globe. Examples of events are festivals, concerts, shows, conferences, sports events, movie launches, etc. There are several event recommendation systems that suggest different events to the users. The popularity of an event plays an important role in event recommendation. If the system knows which event is popular in a region, then that can be recommended to more people in that region. However, since events take place at predefined location and time it is important to identify the popularity much before the occurrence of the event. Early prediction of future popularity is very crucial for the success of any event recommendation system. We plan to use event related discussions in social media as a signal for estimating the popularity of the events. Towards this task, given meta information (time, date, venue, title etc.) about an event, we first try to identify social media posts related to the event. These posts are then passed through several preprocessing stages in a pipeline to detect spam, identify the presence of emotions and their intensities, etc. The preprocessed posts can then be used along with several other contextual parameters to predict the future popularity of the events. Next, events can be recommended to the users by using a recommendation algorithm.
Review on Securing Medical Big Data in Healthcare Cloud

E-health is one of an emerging field in health industry. In order to diagnose a patient, doctors need the medical record of the patient which contains multimedia big data. For efficient access EMR (Electronic Medical Record) should be placed in cloud which also supports mobility.In having many advantages, it faces many security issues as well, of which hacking is considered to be the most serious of healthcare data in cloud. In this paper, a survey on different methods for securing medical big data in cloud is done. The healthcare data can be stored and accessed using a decoy technique.
Fiducial Marker Approach for Biomechanical Smartphone-Based Measurements

Marker-based measurement has been used to assess human body positioning, but human marker tracking has yet to make the transition from the laboratory to personal computing devices, such as smartphones. A novel smartphone-based approach could use a fiducial marker system. Fiducial markers are applicable to augmented reality, robotics, and other applications where a camera-object pose is required and tracked. However, few fiducial systems can be implemented on a mobile phone because of the processing requirements for identifying and tracking the tags in realtime. In augmented reality, virtual information is shared with the real world to further enhance a person's view of the environment; therefore, this illusion is directly associated with good registration of both virtual and real worlds. Measurement applications also require accurate and fast registration so that real objects are in alignment with virtual objects in real-time. Our research reviewed and evaluated various fiducial marker systems by developing an Android mobile application for real-time biomechanical measurement. A test was designed for two nominated fiducial systems to compare their speed and robustness on the mobile phone. AprilTag2 was selected as the best fiducial marker option for this application.
Blockchain Ethics Research

Blockchain is being widely adopted far beyond finance into numerous domains of society and promises unprecedented potential to disrupt organizations, businesses, industries, and economies. However, blockchain is still in its infancy and its future is highly controversial, arousing phenomenal enthusiasm, high expectations, and even intense criticism. The possible impacts of blockchain and its applications on the society could be fundamental and revolutionary, inevitably bringing unpredicted ethical challenges in the foreseeable future. Identifying the ethical challenges of blockchain is urgent and critically needed to ensure that blockchain is adopted ethically. However, discussions on the ethics of blockchain are largely insufficient, which leaves a void of theoretical understanding so far. In this paper, we provide a systematic discussion on the ethics of blockchain applications and map the main social challenges raised by its technology and applications. The paper starts with a review of the technological concepts and applications of blockchain. Then, it overviews the current research on the ethics of technologies and general research on blockchain to briefly introduce the authors' approach. Afterwards, a conceptual model of blockchain ethics research is developed. This research hopes to serve as an initial roadmap for the study of blockchain ethics, and to raise timely awareness and stimulate further debate on the ethics of blockchain in the IS community.
Hybrid of genetic algorithm and krill herd for software clustering problem

Clustering techniques are usually utilized to partition a software system, aiming to understand it. Understanding a program helps to maintain the legacy source code. Since the partitioning of a software system is an NP-hard problem, using the evolutionary approaches seems reasonable. Krill herd (KH) evolutionary algorithm is an effective algorithm for solving optimization problems with continuous state space which imitates the individual and group behavior of krill. According to its nature, is unable to solve the discrete space problems. The main advantage of this algorithm is to keep the information flow between different individuals during the evolutionary process. Genetic algorithm (GA) is an evolutionary algorithm utilizing the search techniques to find the closest solution to optimal; however, its main problem is a lack of strong effective information flow between different generations. This paper proposes a new evolutionary method, named GAKH, for software clustering inspired by Krill herd and Genetic algorithm. In the proposed evolutionary algorithm, the strengths of these two algorithms have been utilized and better results have been achieved in software clustering by changing GA cycle and operators, adding swarm intelligence and inspiring from Krill movements. The initial results achieved from the application of the proposed algorithm on ten software systems indicate higher quality results of clustering compared to other algorithms.
Object Detection and Tracking Approaches for Video Surveillance Over Camera Network

Object detection and tracking are the most challenging part of any computer vision applications. In computer vision, video surveillance is a popular research area in a dynamic environment, particularly for security reasons. The video surveillance technology plays a crucial role to prevent crime, terrorism etc. The video outputs are filtered and processed by human operators and in case of a forensic, the high volume of data made it difficult to track any object. This work has been done with an aim to reduce the effort of human operators with an increase in the response time to forensic events. It involves designing of an efficient object tracking system for simple environments where the camera is static, background is simple and no similar object to the one being tracked is present. The system is provided with network configuration of the cameras and roads of the surveillance area, videodumps and an image of the object to be tracked. It tracks the objects through the videos and dumps the tracked portions of the videos where the object was present.
Field-of-view prediction in 360-degree videos with attention-based neural encoder-decoder networks

In this paper, we propose attention-based neural encoder-decoder networks for predicting user Field-of-View (FoV) in 360-degree videos. Our proposed prediction methods are based on the attention mechanism that learns the weighted prediction power of historical FoV time series through end-to-end training. Attention-based neural encoder-decoder networks do not involve recursion, thus can be highly parallelized during training. Using publicly available 360-degree head movement datasets, we demonstrate that our FoV prediction models outperform the state-of-art FoV prediction models, achieving lower prediction error, higher training throughput, and faster convergence. Better FoV prediction leads to reduced bandwidth consumption, better video quality, and improved user quality of experience.
All the Tea in China

We picture various forms of engagement with Tea (and Coffee). The images are selected from a large and ongoing collection of street photography and posed images along this theme. The images as presented here are prompts to interaction design inspirations, after the style of [1]. The text is deliberately minimal, as part of a focus on visually rich content for and advancement of the form of primarily photographic pictorials in HCI.
How a Stylistic, Machine-Generated Thesaurus Impacts a Writer's Process

Writers regularly use a thesaurus to help them write well; the thesaurus is one of the few widespread writing support tools and many writers find it integral to their writing practice. A normal thesaurus is hand-crafted and structured around strict synonymy for a given word sense. However, writers rarely look for a perfectly synonymous word -- instead they have additional ideas or constraints, such as words that are less cliche, more specific, or less gendered. Poets describe their usage as searching for words that "hold more interesting connotations." We present a machine learning approach to thesaurus generation, using word embeddings, that leverages stylistically distinct corpora -- such as naturalist writing, novels by a particular author, or writing from a technical discipline. We show examples of how stylistic thesauruses differ from each other and from a regular thesaurus, as well as preliminary responses from two writers who are given multiple stylistic thesauruses. Writers describe these thesauruses as reflective of style, unique from each other, and more exploratory and associative than a regular thesaurus. They also describe an increased attention to connotation. We outline plans for quantitative evaluation of stylistic thesauruses, and user studies to understand their impact on specific tasks.
Creativity as Computation: Teaching Design in the Age of Automation

Graphic design and user-experience design are augmented by computing, including tools that use artificial intelligence (AI) to infer intent and automate various activities. The efficacy of these tools conceals an ideological commitment to human creativity that is, at its core, explainable via computational processes. Design education often unintentionally supports this ideology. This paper first examines the ideology of creativity and the designerly act as computational. Second, it examines the technical research and corporate rhetoric that support this ideology and its connection to a broader set of ideas about the relations between people and computing. Finally, some possibilities for pedagogical intervention are proposed.
Immersion in Games and Gamification

The term immersion and what does it imply in the context of games are the concepts psychologists, game designers have been studying for decades. ImmersionImmersion" \b \i is often termed as presence in the gaming world, which is of various kinds in terms of how media is perceived. The spatial presence or immersion is closest to what game designers think of as immersion. The immersion in game designGame design" \b \i is that a game creates a spatial presence when the player starts to feel that he or she is there in the world created by game. Players who experience immersion are likely to just focus on the choices that seem meaningful in the context of the imaginary world. This chapter talks about what immersion is, how it is related to game design and gamification, its purpose, effectiveness, and associated elements and concepts. It also highlights how immersion can enhance the engagementEngagement" \b \i for the users.
Focal Model in the Pattern Recognition Problem

The pattern recognition problem in the focal paradigm is considered, which makes it possible to apply the focal model both at the learning stage and at the identification stage. The theoretical substantiation and methods of algorithmic realization of the description of classes and their boundaries with the help of the focal model of representation of smooth curves and surfaces by multifocal lemniscates are given. As a result, at the learning stage for each class in the feature space a multipolar space with a focal distance is formed, in which the class boundary is described by a lemniscate and is an isometric surface. An important advantage is that the focal classification space also forms a continuous affiliation function. At the decision-making stage, the advantage of the focal model is that identification is an elementary operation of calculating the focal distance of a given point and comparing its value with the boundary parameter. Thus, the multidimensional classification problem reduces to optimization and decision-making in one-dimensional space. The complexity of the traditional solution, resulting in a fragmented description of class boundaries, is compensated by a high level of computer power that allow successfully cope with practical classification tasks. At the same time, a person has, in addition to a strong applied rationale, the desire for adequacy and organicity of the approach, the result of which is simplicity and sensibility. Such a solution of the classical recognition problem in the focal paradigm is proposed in this paper.
STRETCH

Despite the established scientific knowledge on efficient parallel and elastic data stream processing, it is challenging to combine generality and high level of abstraction (targeting ease of use) with fine-grained processing aspects (targeting efficiency) in stream processing frameworks. Towards this goal, we propose STRETCH, a framework that aims at guaranteeing (i) high efficiency in throughput and latency of stateful analysis and (ii) fast elastic reconfigurations (without requiring state transfer) for intra-node streaming applications. To achieve these, we introduce virtual shared-nothing parallelization and propose a scheme to implement it in STRETCH, enabling users to leverage parallelization techniques while also taking advantage of shared-memory synchronization, which has been proven to boost the scaling-up of streaming applications while supporting determinism. We provide a fully-implemented prototype and, together with a thorough evaluation, correctness proofs for its underlying claims supporting determinism and a model (also validated empirically) of virtual shared-nothing and pure shared-nothing scalability behavior. As we show, STRETCH can match the throughput and latency figures of the front of state-of-the-art solutions, while also achieving fast elastic reconfigurations (taking only a few milliseconds).
Machine Autonomy: Definition, Approaches, Challenges and Research Gaps

The processes that constitute the designs and implementations of AI systems such as self-driving cars, factory robots and so on have been mostly hand-engineered in the sense that the designers aim at giving the robots adequate knowledge of its world. This approach is not always efficient especially when the agent’s environment is unknown or too complex to be represented algorithmically. A truly autonomous agent can develop skills to enable it to succeed in such environments without giving it the ontological knowledge of the environment a priori. This paper seeks to review different notions of machine autonomy and presents a definition of autonomy and its attributes. The attributes of autonomy as presented in this paper are categorised into low-level and high-level attributes. The low-level attributes are the basic attributes that serve as the separating line between autonomous and other automated systems while the high-level attributes can serve as a taxonomic framework for ranking the degrees of autonomy of any system that has passed the low-level autonomy. The paper reviews some AI techniques as well as popular AI projects that focus on autonomous agent designs in order to identify the challenges of achieving a true autonomous system and suggest possible research directions.
Construction and Application of Digital Teaching Resources in Regional Basic Education &amp;lt;br/&amp;gt;—Taking Physical Education Courses as an Example

Digital teaching resources are the core cornerstone of educational informationization. Their construction and application level determine the pace of educational modernization. Digital teaching resources for basic education are an effective guarantee to solve the problem of educational equity in urban and rural areas and promote the co-construction and sharing of high-quality scientific and technological education information resources. Based on the reality of uneven distribution of basic education resources in different regions of China, taking physical education courses in Yueyang District of Hunan Province as an example, this paper studies the problems existing in the construction and application of digital teaching resources in regional basic education, and puts forward corresponding countermeasures and suggestions, which can provide reference for the reform and development of information-based teaching in basic education in relevant regions of China.
Explaining Need-based Educational Recommendations Using Interactive Open Learner Models

Students might pursue different goals throughout their learning process. For example, they might be seeking new material to expand their current level of knowledge, repeating content of prior classes to prepare for an exam, or working on addressing their most recent misconceptions. Multiple potential goals require an adaptive e-learning system to recommend learning content appropriate for students' intent and to explain this recommendation in the context of this goal. In our prior work, we explored explainable recommendations for the most typical 'knowledge expansion goal". In this paper, we focus on students' immediate needs to remedy misunderstandings when they solve programming problems. We generate learning content recommendations to target the concepts with which students have struggled more recently. At the same time, we produce explanations for this recommendation goal in order to support students' understanding of why certain learning activities are recommended. The paper provides an overview of the design of this explainable educational recommender system and describes its ongoing evaluation