Reviewerly/exHarmony huggingface dataset:
Relevance-driven Clustering for Visual Information Retrieval on Twitter

Geo-temporal visualization of Twitter search results is a challenging task since the simultaneous display of all matching tweets would result in a saturated and unreadable display. In such settings, clustering search results can assist users to scan only a few coherent groups of related tweets rather than many individual tweets. However, in practice, the use of unsupervised clustering methods such as K -Means does not necessarily guarantee that the clusters themselves are relevant. Therefore, we develop a novel method of relevance-driven clustering for visual information retrieval to supply users with highly relevant clusters representing different information perspectives of their queries. We specifically propose a Visual Twitter Information Retrieval (Viz-TIR) tool for relevance-driven clustering and ranking of Twitter search results. At the heart of Viz-TIR is a fast greedy algorithm that optimizes an approximation of an expected F1-Score metric to generate these clusters. We demonstrate its effectiveness w.r.t. K -Means and a baseline method that shows all top matching results on a scenario related to searching natural disasters in US-based Twitter data spanning 2013 and 2014. Our demo shows that Viz-TIR is easy to use and more precise in extracting geo-temporally coherent clusters given search queries in comparison to K-Means, thus aiding the user in visually searching and browsing social network content. Overall, we believe this work enables new opportunities for the synthesis of information retrieval as well as combined relevance and display-aware optimization techniques to support query-adaptive visual information exploration interfaces.
Reusability in goal modeling: A systematic literature review

Context: Goal modeling is an important instrument for the elicitation, specification, analysis, and validation of early requirements. Goal models capture hierarchical representations of stakeholder objectives, requirements, possible solutions, and their relationships to help requirements engineers understand stakeholder goals and explore solutions based on their impact on these goals. To reuse a goal model and benefit from the strengths of goal modeling, we argue that it is necessary (i) to make sure that analysis and validation of goal models is possible through reuse hierarchies, (ii) to provide the means to delay decision making to a later point in the reuse hierarchy, (iii) to take constraints imposed by other modeling notations into account during analysis, (iv) to allow context dependent information to be modeled so that the goal model can be used in various reuse contexts, and (v) to provide an interface for reuse. Objective: In this two-part systematic literature review, we (i) evaluate how well existing goal modeling approaches support reusability with our five desired characteristics of contextual and reusable goal models, (ii) categorize these approaches based on language constructs for context modeling and connection to other modeling formalisms, and then (iii) draw our conclusions on future research themes. Method: Following guidelines by Kitchenham, the review is conducted on seven major academic search engines. Research questions, inclusion criteria, and categorization criteria are specified, and threats to validity are discussed. A final list of 146 publications and 34 comparisons/assessments of goal modeling approaches is discussed in more detail. Results: Five major research themes are derived to realize reusable goal models with context dependent information. Conclusion: The results indicate that existing goal modeling approaches do not fully address the required capabilities for reusability in different contexts and that further research is needed to fill this gap in the landscape of goal modeling approaches.
Decidability and complexity of the fragments of the modal logic of Allen's relations over the rationals

Interval temporal logics provide a natural framework for temporal reasoning about interval structures over linearly ordered domains, where intervals are taken as first-class citizens.Their expressive power and computational behaviour mainly depend on two parameters: the set of modalities they feature and the linear orders over which they are interpreted.In this paper, we consider all fragments of Halpern and Shoham's interval temporal logic HS with a decidable satisfiability problem over the rationals, and we provide a complete classification of them in terms of their expressiveness and computational complexity by solving the last few open problems.
MemoryReel

This paper presents the design rationale and concept development behind MemoryReel, a tangibly interactive desktop device that records special moments of online social interactions, between couples and friends over a long distance, and supports later reminiscence. In a human-centered design process started with a two-act design inquiry, we developed the design concept and implemented a medium-to-high-fidelity interactive prototype. We then invited 20 participants to experience the prototype and give comments. The design rationale together with the analysis of the exploratory user study findings provides insights on the dimensions and strategies of a design space for digital memories and long-distance relationships, with an emphasis on reminiscence support.
PZnet: Efficient 3D ConvNet Inference on Manycore CPUs

Convolutional nets have been shown to achieve state-of-the-art accuracy in many biomedical image analysis tasks. Many tasks within biomedical analysis domain involve analyzing volumetric (3D) data acquired by CT, MRI and Microscopy acquisition methods. To deploy convolutional nets in practical working systems, it is important to solve the efficient inference problem. Namely, one should be able to apply an already-trained convolutional network to many large images using limited computational resources. In this paper we present PZnet, a CPU-only engine that can be used to perform inference for a variety of 3D convolutional net architectures. PZNet outperforms MKL-based CPU implementations of PyTorch and Tensorflow by more than 3.5x for the popular U-net architecture. Moreover, for 3D convolutions with low featuremap numbers, cloud CPU inference with PZnet outperforms cloud GPU inference in terms of cost efficiency.
Video Action Recognition With an Additional End-to-End Trained Temporal Stream

Detecting actions in videos requires understanding the temporal relationships among frames. Typical action recognition approaches rely on optical flow estimation methods to convey temporal information to a CNN. Recent studies employ 3D convolutions in addition to optical flow to process the temporal information. While these models achieve slightly better results than two-stream 2D convolutional approaches, they are significantly more complex, requiring more data and time to be trained. We propose an efficient, adaptive batch size distributed training algorithm with customized optimizations for training the two 2D streams. We introduce a new 2D convolutional temporal stream that is trained end-to-end with a neural network. The flexibility to freeze some network layers from training in this temporal stream brings the possibility of ensemble learning with more than one temporal streams. Our architecture that combines three streams achieves the highest accuracies as we know of on UCF101 and HMDB51 by systems that do not pretrain on much larger datasets (e.g., Kinetics). We achieve these results while keeping our spatial and temporal streams 4.67x faster to train than the 3D convolution approaches.
7.2 A 20.5TOPS and 217.3GOPS/mm<sup>2</sup> Multicore SoC with DNN Accelerator and Image Signal Processor Complying with ISO26262 for Automotive Applications

Vehicles with features for an advanced driver-assistance system (ADAS) are already available on the market. Technologies for image recognition are essential elements for ADAS applications, and several SoCs performing image recognition for ADAS have been developed [1-3]. As vehicles that support ADAS become more complicated, these technologies need improvement. Recently, deep neural networks (DNNs) have achieved higher recognition accuracy than traditional feature-based matching algorithms, and can be applied to applications such as road detection. Consequently, several image recognition SoCs with DNN processors [4-5] have been developed. Functional safety is important for automotive applications, so several SoCs, such as [2], comply with ISO26262. The requirements for SoCs that support ADAS features are: high performance for detecting various objects, low power use to keep execution stable in the rapidly changing environment of moving vehicles, and high safety to avoid serious accidents.
Designing Interactive Robotic Games based on Mixed Reality Technology

This paper focuses on an emerging research area represented by robotic gaming and aims to explore the design space of interactive games that combine commercial-off-the-shelf robots and mixed reality. To this purpose, a software platform is developed which allows players to interact with both physical elements and virtual content projected on the ground. A game is then created to show designers how to maximize opportunities offered by such a technology and to build playful experiences.
Blockchain Technology for Public Utilities

The paper presents the needs to improve the controlling systems in public utilities. One example was the use of blockchain technology in the work of the management company that supports the buildings. The method of such technology in the public utilities management system ensures the transparency of mutual obligations and information security between the consumer and the service provider. This leads to the embodiment in reality of the basic principles of effective relationships: the balance of interests of market participants and the openness of interaction.
Segmentation of follicles from CD8-stained slides of follicular lymphoma using deep learning

Follicular Lymphoma (FL) is the second most common subtype of lymphoma in the Western World. It is a low-grade lymphoma arising from Germinal Centre (GC) B cells. The neoplasm predominantly consists of back-to-back arrangement of nodules or follicles of transformed GC B cells with the replacement of lymph node architecture and loss of normal cortex and medullary differentiation, which is preserved in non-neoplastic or reactive lymph node. There is a growing interest in studying different cell subsets inside and on the periphery of the follicles to direct curative therapies and minimize treatment-related complications. To facilitate this analysis, we develop an automated method for follicle detection from images of CD8 stained histopathological slides. The proposed method is trained on eight whole digital slides. The method is inspired by U-net to segment follicles from the whole slide images. The results on an independent dataset resulted in an average Dice similarity coefficient of 85.6% when compared to an expert pathologist’s annotations. We expect that the method will play a considerable role for comparing the ratios of different subsets of cells inside and at the periphery of the follicles.
The Service Value Method for Design of Energy Access Systems in the Global South

This paper presents the service value method (SVM) as a novel means to gather and interpret end-user needs, aspirations, and contextual factors to improve engineering design practice of energy access systems for the Global South. The method adopts a service-oriented approach and consists of a rapid and effective field exercise to gather qualitative and quantitative data from end users in focus groups. This exercise is suitable for enabling end-user participation in Global South contexts. The data are interpreted as service maps that capture end-user preferences to inform tradeoffs of different design criteria, guiding the preliminary design of the energy system. The method ensures end-user needs and contexts are integrated into the design process early on. A case study is presented, where the SVM was used to design solar nanogrids in Kenya and Bangladesh.
Practical Quantum Private Query with Classical Participants

Quantum key distribution (QKD)-based quantum private query (QPQ) is a practical application of QKD, which relaxes the security condition of perfectly concealing a private query to a cheating-sensitive strategy. We propose a QPQ protocol based on the delegated QKD scheme (DQKD-based QPQ), in which two almost 'classical' clients (data user and database owner) can establish a 1-out-of-N oblivious key with the help of a cloud server with full quantum ability. Concretely, the two classical participants in the DQKD-based QPQ only need to access the quantum channel and reorder qubits, and the costly quantum operations, quantum state preparation and measurement are outsourced to a full quantum server in the cloud without leaking participants' privacy. The proposed protocol not only provides a cloud-based framework of QKD-based QPQ, but also obtains better security by a real-time security check, which can protect the security of the database and user against all potential attacks even if the quantum server is assumed to be a powerfully untrusted adversary.
Document image recognition algorithm based on similarity metric robust to projective distortions for mobile devices

The paper presents an algorithm for document image recognition robust to projective distortions. This algorithm is based on a similarity metric, which is learned using Siamese architecture. The idea of training Siamese networks is to build a function of converting the image into a space where a distance function corresponding to a pre-defined metric approximates the similarity between objects of initial space. During learning the loss function tries to minimize the distance between pairs of object from the same class and maximize it between the ones from different classes. A convolutional network is used for mapping initial space to the target one. This network lets to construct a feature vector in target space for each class. Classification of objects is performed using the mapping function and finding the nearest feature vector. The proposed algorithm achieved recognition quality comparable to classifying convolutional network on an open dataset of document images MIDV-500 [1]. Another important advantage of this method is the possibility of one-shot learning that is also shown in the paper.
Analyzing Electrooculography (EOG) for Eye Movement Detection

Although the cognitive parts of their brains are intact, some individual scan only interact with the outside environment through eye movements. Those people suffer from severe motor disabilities preventing them from moving all their limbs. Recently, Human Computer Interfaces (HCI) has emerged to help these people by providing them a new way for communication. These interfaces are based on detecting eye movements. Electro-oculogram (EOG) records eye movements through few electrodes placed around the eyes vertically and horizontally. In this paper, EOG vertical and horizontal signals are analyzed to detect four eye movements (left, right, up and down) along with blinking. Three statistical features are extracted from filtered EOG signals. Extracted features from horizontal and vertical EOG signals are concatenated to form final feature vector. K Nearest Neighbor (KNN), Linear Discriminant Analysis (LDA), Multinomial Logistic Regression (MLR), Naïve Bayes (NB), Decision Trees and Support Vector Machines (SVM) are six classifiers that are evaluated in this study. The results reveal the superiority of SVM Classifier in providing the best average accuracy.
Friction, snake oil, and weird countries: Cybersecurity systems could deepen global inequality through regional blocking

In this moment of rising nationalism worldwide, governments, civil society groups, transnational companies, and web users all complain of increasing regional fragmentation online. While prior work in this area has primarily focused on issues of government censorship and regulatory compliance, we use an inductive and qualitative approach to examine targeted blocking by corporate entities of entire regions motivated by concerns about fraud, abuse, and theft. Through participant-observation at relevant events and intensive interviews with experts, we document the quest by professionals tasked with preserving online security to use new machine-learning based techniques to develop a “fairer” system to determine patterns of “good” and “bad” usage. However, we argue that without understanding the systematic social and political conditions that produce differential behaviors online, these systems may continue to embed unequal treatments, and troublingly may further disguise such discrimination behind more complex and less transparent automated assessment. In order to support this claim, we analyze how current forms of regional blocking incentivize users in blocked regions to behave in ways that are commonly flagged as problematic by dominant security and identification systems. To realize truly global, non-Eurocentric cybersecurity techniques would mean incorporating the ecosystems of service utilization developed by marginalized users rather than reasserting norms of an imagined (Western) user that casts aberrations as suspect.
An Architecture for Mobile-based Assessment Systems in Smart Learning Environments

Assessment is a fundamental part of the learning process and therefore a key aspect in smart learning environments. Despite the existence of some architectures and frameworks for supporting assessment with mobile technologies, there is still a need of more research on how these systems should be designed for smart learning environments. In this paper we extend previous research on mobile-based assessment to introduce an architecture to inform the design and development of mobile-based assessment systems for smart learning environments to foster smart learning. The architecture consists of a client side (mobile app) and a server side web application. Preliminary results obtained from an evaluation study shows high levels of students’ acceptance and intrinsic motivation after using a mobile-based assessment application that implements the architecture.
Survey of data remaining on second hand memory cards in the UK

This study was an investigation into the level and type of information that remained on memory cards (SD, Micro SD) that were purchased from the second hand market in the UK in 2018. The storage capacity of memory cards has continued to increase in capacity at a rapid rate and had reached 512GB at the time of the study. Memory cards are now commonly used in a wide range of consumer devices, but are used, in the main, in mobile phones, tablet computers, cameras, Satellite Navigation (SatNav) systems, dashboard cameras (dashcams), drones and multimedia devices. This study was carried out on 100 s hand memory cards. The aim of the study was to determine the proportion of memory cards that still contained data and of those that did, what type of data they contained. The study also looked, where data was found, at whether any attempt had been made to remove it. The investigation showed that sensitive and personal data was present on 67 percent of the memory cards. While some of the memory cards showed evidence of attempts to remove the data, in many instances there was no such evidence to suggest that the seller had made any attempted to remove it.
On the Importance of Feature Aggregation for Face Reconstruction

The goal of this work is to seek principles of designing a deep neural network for 3D face reconstruction from a single image. To make the evaluation simple, we generated a synthetic dataset and used it for evaluation. We conducted extensive experiments using an end-to-end face reconstruction algorithm using E2FAR and its variations, and analyzed the reason why it can be successfully applied for 3D face reconstruction. From the comparative studies, we conclude that feature aggregation from different layers is a key point to training better neural networks for 3D face reconstruction. Based on these observations, a face reconstruction feature aggregation network (FR-FAN) is proposed, which obtains significant improvements compared with baselines on the synthetic validation set. We evaluate our model on existing popular indoor and in-the-wild 2D-3D datasets. Extensive experiments demonstrate that FR-FAN performs 16.50% and 9.54% better than E2FAR on BU-3DFE and JNU-3D, respectively. Finally, the sensitivity analysis we performed on controlled datasets demonstrates that our designed network is robust to large variations of pose, illumination, and expressions.
Local and Global Query Expansion for Hierarchical Complex Topics

In this work we study local and global methods for query expansion for multifaceted complex topics. We study word-based and entity-based expansion methods and extend these approaches to complex topics using fine-grained expansion on different elements of the hierarchical query structure. For a source of hierarchical complex topics we use the TREC Complex Answer Retrieval (CAR) benchmark data collection. We find that leveraging the hierarchical topic structure is needed for both local and global expansion methods to be effective. Further, the results demonstrate that entity-based expansion methods show significant gains over word-based models alone, with local feedback providing the largest improvement. The results on the CAR paragraph retrieval task demonstrate that expansion models that incorporate both the hierarchical query structure and entity-based expansion result in a greater than 20% improvement over word-based expansion approaches.
On enhancing the robustness of timeline summarization test collections

Timeline generation systems are a class of algorithms that produce a sequence of time-ordered sentences or text snippets extracted in real-time from high-volume streams of digital documents (e.g. news articles), focusing on retaining relevant and informative content for a particular information need (e.g. topic or event). These systems have a range of uses, such as producing concise overviews of events for end-users (human or artificial agents). To advance the field of automatic timeline generation, robust and reproducible evaluation methodologies are needed. To this end, several evaluation metrics and labeling methodologies have recently been developed - focusing on information nugget or cluster-based ground truth representations, respectively. These methodologies rely on human assessors manually mapping timeline items (e.g. sentences) to an explicit representation of what information a 'good' summary should contain. However, while these evaluation methodologies produce reusable ground truth labels, prior works have reported cases where such evaluations fail to accurately estimate the performance of new timeline generation systems due to label incompleteness. In this paper, we first quantify the extent to which the timeline summarization test collections fail to generalize to new summarization systems, then we propose, evaluate and analyze new automatic solutions to this issue. In particular, using a depooling methodology over 19 systems and across three high-volume datasets, we quantify the degree of system ranking error caused by excluding those systems when labeling. We show that when considering lower-effectiveness systems, the test collections are robust (the likelihood of systems being miss-ranked is low). However, we show that the risk of systems being mis-ranked increases as the effectiveness of systems held-out from the pool increases. To reduce the risk of mis-ranking systems, we also propose a range of different automatic ground truth label expansion techniques. Our results show that the proposed expansion techniques can be effective at increasing the robustness of the TREC-TS test collections, as they are able to generate large numbers missing matches with high accuracy, markedly reducing the number of mis-rankings by up to 50%.