Reviewerly/exHarmony huggingface dataset:
Compact and Accurate Scene Text Detector

Scene text detection is the task of detecting word boxes in given images. The accuracy of text detection has been greatly elevated using deep learning models, especially convolutional neural networks. Previous studies commonly aimed at developing more accurate models, but their models became computationally heavy and worse in efficiency. In this paper, we propose a new efficient model for text detection. The proposed model, namely Compact and Accurate Scene Text detector (CAST), consists of MobileNetV2 as a backbone and balanced decoder. Unlike previous studies that used standard convolutional layers as a decoder, we carefully design a balanced decoder. Through experiments with three well-known datasets, we then demonstrated that the balanced decoder and the proposed CAST are efficient and effective. The CAST was about 1.1x worse in terms of the F1 score, but 30∼115x better in terms of floating-point operations per second (FLOPS).
HeadCross

We propose HeadCross, a head-based interaction method to select targets on VR and AR head-mounted displays (HMD). Using HeadCross, users control the pointer with head movements and to select a target, users move the pointer into the target and then back across the target boundary. In this way, users can select targets without using their hands, which is helpful when users' hands are occupied by other tasks, e.g., while holding the handrails. However, a major challenge for head-based methods is the false positive problems: unintentional head movements may be incorrectly recognized as HeadCross gestures and trigger the selections. To address this issue, we first conduct a user study (Study 1) to observe user behavior while performing HeadCross and identify the behavior differences between HeadCross and other types of head movements. Based on the results, we discuss design implications, extract useful features, and develop the recognition algorithm for HeadCross. To evaluate HeadCross, we conduct two user studies. In Study 2, we compared HeadCross to the dwell-based selection method, button-press method, and mid-air gesture-based method. Two typical target selection tasks (text entry and menu selection) are tested on both VR and AR interfaces. Results showed that compared to the dwell-based method, HeadCross improved the sense of control; and compared to two hand-based methods, HeadCross improved the interaction efficiency and reduced fatigue. In Study 3, we compared HeadCross to three alternative designs of head-only selection methods. Results show that HeadCross was perceived to be significantly faster than the alternatives. We conclude with the discussion on the interaction potential and limitations of HeadCross.
Generalization error analysis for deep convolutional neural network with transfer learning in breast cancer diagnosis

Deep convolutional neural network (DCNN), now popularly called artificial intelligence (AI), has shown the potential to improve over previous computer-assisted tools in medical imaging developed in the past decades. A DCNN has millions of free parameters that need to be trained, but the training sample set is limited in size for most medical imaging tasks so that transfer learning is typically used. Automatic data mining may be an efficient way to enlarge the collected data set but the data can be noisy such as incorrect labels or even a wrong type of image. In this work we studied the generalization error of DCNN with transfer learning in medical imaging for the task of classifying malignant and benign masses on mammograms. With a finite available data set, we simulated a training set containing corrupted data or noisy labels. The balance between learning and memorization of the DCNN was manipulated by varying the proportion of corrupted data in the training set. The generalization error of DCNN was analyzed by the area under the receiver operating characteristic curve for the training and test sets and the weight changes after transfer learning. The study demonstrates that the transfer learning strategy of DCNN for such tasks needs to be designed properly, taking into consideration the constraints of the available training set having limited size and quality for the classification task at hand, to minimize memorization and improve generalizability.
Nudging personalized password policies by understanding users’ personality

Password composition policies are used to prevent users from picking weak passwords. A website usually provides a unified password policy for each user but ignores the fact that people have a variety of preferences due to individual differences, which makes it difficult to achieve the expected strong password goals. In order to improve the effectiveness of password composition policies, we propose a dynamic personalized password policy (DPPP), which can personally recommend different password policies according to the user’s personality traits. We conduct an online study to evaluate the security and usability of DPPP and the two common password composition policies Basic8 and 3class8. The study results show that DPPP is more effective than Basic8 and 3class8 in resisting online and offline guessing attacks. DPPP is inferior to Basic8 and 3class8 only in the creation time and outperforms 3class8 in creating difficulty with significant differences.
Large-Scale Talent Flow Embedding for Company Competitive Analysis

Recent years have witnessed the growing interests in investigating the competition among companies. Existing studies for company competitive analysis generally rely on subjective survey data and inferential analysis. Instead, in this paper, we aim to develop a new paradigm for studying the competition among companies through the analysis of talent flows. The rationale behind this is that the competition among companies usually leads to talent movement. Along this line, we first build a Talent Flow Network based on the large-scale job transition records of talents, and formulate the concept of “competitiveness” for companies with consideration of their bi-directional talent flows in the network. Then, we propose a Talent Flow Embedding (TFE) model to learn the bi-directional talent attractions of each company, which can be leveraged for measuring the pairwise competitive relationships between companies. Specifically, we employ the random-walk based model in original and transpose networks respectively to learn representations of companies by preserving their competitiveness. Furthermore, we design a multi-task strategy to refine the learning results from a fine-grained perspective, which can jointly embed multiple talent flow networks by assuming the features of company keep stable but take different roles in networks of different job positions. Finally, extensive experiments on a large-scale real-world dataset clearly validate the effectiveness of our TFE model in terms of company competitive analysis and reveal some interesting rules of competition based on the derived insights on talent flows.
Improving Speed and Security in Updatable Encryption Schemes

Periodic key rotation is a common practice designed to limit the long-term power of cryptographic keys. Key rotation refers to the process of re-encrypting encrypted content under a fresh key, and overwriting the old ciphertext with the new one. When encrypted data is stored in the cloud, key rotation can be very costly: it may require downloading the entire encrypted content from the cloud, re-encrypting it on the client's machine, and uploading the new ciphertext back to the cloud. An updatable encryption scheme is a symmetric-key encryption scheme designed to support efficient key rotation in the cloud. The data owner sends a short update token to the cloud. This update token lets the cloud rotate the ciphertext from the old key to the new key, without learning any information about the plaintext. Recent work on updatable encryption has led to several security definitions and proposed constructions. However, existing constructions are not yet efficient enough for practical adoption, and the existing security definitions can be strengthened. In this work we make three contributions. First, we introduce stronger security definitions for updatable encryption (in the ciphertext-dependent setting) that capture desirable security properties not covered in prior work. Second, we construct two new updatable encryption schemes. The first construction relies only on symmetric cryptographic primitives, but only supports a bounded number of key rotations. The second construction supports a (nearly) unbounded number of updates, and is built from the Ring Learning with Errors (RLWE) assumption. Due to complexities of using RLWE, this scheme achieves a slightly weaker notion of integrity compared to the first. Finally, we implement both constructions and compare their performance to prior work. Our RLWE-based construction is 200 $$\times $$ faster than a prior proposal for an updatable encryption scheme based on the hardness of elliptic curve DDH. Our first construction, based entirely on symmetric primitives, has the highest encryption throughput, approaching the performance of AES, and the highest decryption throughput on ciphertexts that were re-encrypted fewer than fifty times. For ciphertexts re-encrypted over fifty times, the RLWE construction dominates it in decryption speed.
Full Reference Light Field Image Quality Evaluation Based on Angular-Spatial Characteristic

The quality evaluation is an indispensable link in light field (LF) image processing. Most of existing LF objective evaluation indexes do not make effective use of the angular characteristic of LF, so the evaluation results are unsatisfactory. In this letter, the quality evaluation of LF image is constructed based on human visual system (HVS) and LF angular-spatial characteristics. Based on the fact that HVS has different sensitivity to different parallaxes, we assume that LF image quality perceived by human eyes has the optimal parallax range. A dual-fan filter is used to constrain the parallax range. Then, the overall quality of the LF is represented by combining the spatial and angular quality, which performed from the central sub-aperture image and the focus stack, respectively. In addition, because the difference of Gaussian (DoG) operator can simulate the process of extracting texture structure by human eyes. The structural similarity of DoG texture feature is utilized in the spatial quality evaluation. Extensive comparison experiments show that the proposed method is more consistent with the characteristics of LF.
A Survey of Figurative Language and Its Computational Detection in Online Social Networks

The frequent usage of figurative language on online social networks, especially on Twitter, has the potential to mislead traditional sentiment analysis and recommender systems. Due to the extensive use of slangs, bashes, flames, and non-literal texts, tweets are a great source of figurative language, such as sarcasm, irony, metaphor, simile, hyperbole, humor, and satire. Starting with a brief introduction of figurative language and its various categories, this article presents an in-depth survey of the state-of-the-art techniques for computational detection of seven different figurative language categories, mainly on Twitter. For each figurative language category, we present details about the characterizing features, datasets, and state-of-the-art computational detection approaches. Finally, we discuss open challenges and future directions of research for each figurative language category.
Recommender System Based on Temporal Models: A Systematic Review

Over the years, the recommender systems (RS) have witnessed an increasing growth for its enormous benefits in supporting users’ needs through mapping the available products to users based on their observed interests towards items. In this setting, however, more users, items and rating data are being constantly added to the system, causing several shifts in the underlying relationship between users and items to be recommended, a problem known as concept drift or sometimes called temporal dynamics in RS. Although the traditional techniques of RS have attained significant success in providing recommendations, they are insufficient in providing accurate recommendations due to concept drift problems. These issues have triggered a lot of researches on the development of dynamic recommender systems (DRSs) which is focused on the design of temporal models that will account for concept drifts and ensure more accurate recommendations. However, in spite of the several research efforts on the DRSs, only a few secondary studies were carried out in this field. Therefore, this study aims to provide a systematic literature review (SLR) of the DRSs models that can guide researchers and practitioners to better understand the issues and challenges in the field. To achieve the aim of this study, 87 papers were selected for the review out of 875 total papers retrieved between 2010 and 2019, after carefully applying the inclusion/exclusion and the quality assessment criteria. The results of the study show that concept drift is mostly applied in the multimedia domain, then followed by the e-commerce domain. Also, the results showed that time-dependent neighborhood models are the popularly used temporal models for DRS followed by the Time-dependent Matrix Factorization (TMF) and time-aware factors models, specifically Tensor models, respectively. In terms of evaluation strategy, offline metrics such as precision and recalls are the most commonly used approaches to evaluate the performance of DRS.
Going above and beyond: How intermediaries enhance change in emerging economy institutions to facilitate small to medium enterprise development

Abstract Research summary Despite knowledge about the importance of intermediaries in filling institutional voids, we do not understand how intermediaries facilitate institutional change. This research demonstrates how intermediaries use six rhetorical legitimation strategies—logos, ethos, pathos, autopoiesis, teleological, and anthropos appeals—to influence institutional change in Abia, Nigeria, that facilitate small‐to‐medium‐enterprise (SME) development in the region. While the first five rhetorical legitimation strategies are used in both developed and emerging economies, the sixth strategy—anthropos appeals, which reflect the communal character of many African cultures—seems to be unique in this context. Our findings contribute to the literature by extending our understanding of the role of rhetorical legitimation strategies in bringing about institutional change, use of rhetoric to communicate entrepreneurial opportunities, and significance of nonmarket strategies for SMEs. Managerial summary SMEs influence economic development in emerging economies but face many constraints. This research examines how intermediaries help reduce constraints SMEs in Nigeria encounter by facilitating institutional changes, through the use of six legitimation strategies (logos, pathos, ethos, autopoiesis, teleological, and anthropos). Also, this research explains how the institutional changes encourage the government to fill voids that generate beneficial outcomes to SMEs, emergent local intermediaries, the general public, and the government itself. Finally, this research finds that market actors in Nigeria use an Anthropos legitimation strategy when appealing to individuals in the local market to help with SME development. This strategy, which taps into the communal character of many African cultures and interestingly has some parallels with Confucianism philosophy, seems to be unique in emerging economies.
Limit-Cycle-Based Mutant Multiobjective Pigeon-Inspired Optimization

This article presents a limit-cycle-based mutant multiobjective pigeon-inspired optimization (PIO). In this algorithm, the limit-cycle-based mechanism is devised to consider the factors that affect the flight of pigeons to simplify the multiobjective PIO algorithm. The mutant mechanism is incorporated to strengthen the exploration capability in the evolutionary process. Additionally, the application of the dual repository makes the nondominated solutions stored and selected to guide the flight of pigeons. Attributed to the limit-cycle-based mutant mechanisms, this algorithm not only obtains the faster convergence speed and higher accuracy but also improves its population diversity. To confirm the universal application of this algorithm, theoretical analysis of the convergence is discussed in this article. Finally, comparative experiments of our proposed algorithm and other five multiobjective methods are conducted to verify the accuracy, efficiency, and convergence stability of the proposed algorithm.
Fair sequential group recommendations

Recommender systems have been incorporated in our everyday life; from music to health recommendations, recommender systems have enhanced the users' experience. At the same time, with the expansion of social media, it is now easier than ever to form groups of people. As such, group recommenders have become more popular. Often, we consider the interaction between a group and the recommender system as a stand-alone process; the group requests some suggestions from the system and the system answers without any considerations to past interactions. A more realistic scenario is for a system to require access to history logs, and take them into account when recommending items for a group of users. Not only what items the system previously had recommended, but also, how well were these items received by the members of the group. In this work, we propose a sequential group recommender, which is aware of the past interactions of the group with the system. We introduce the notion of satisfaction that describes how relevant are the recommended items to each member of the group. We utilize satisfaction in a novel aggregation method that achieves to make our model fair for all members of the group. We show with experimental results that the typical group recommendation approaches are substandard to our proposed method.
Immersion and Coherence: Research Agenda and Early Results

Presence has been studied in the context of virtual environments for nearly thirty years, but the field has yet to reach consensus on even basic issues of definition and measurement, and there are many open research questions. We gather many of these open research questions and systematically group them according to what we believe are five key constructs that inform user experience in virtual environments: immersion, coherence, Place Illusion, Plausibility Illusion, and presence. We also report on the design and results of a study that investigated the effects of immersion and coherence on user experience in a stressful virtual visual cliff environment. In this article, each participant experienced a given VE in one of four conditions chosen from a 2x2 design: high or low levels of immersion and high or low levels of coherence. We collected both questionnaire-based and physiological metrics. Several existing presence questionnaires could not reliably distinguish the effects of immersion from those of coherence. They did, however, indicate that high levels of both together result in higher presence, compared any of the other three conditions. This suggests that “breaks in PI” and “breaks in Psi” belong to a broader category of “breaks in experience,” any of which result in a degraded user experience. Participants' heart rates responded markedly differently in the two coherence conditions; no such difference was observed across the immersion conditions. This indicates that a VE that exhibits unusual or confusing behavior can cause stress in a user that affects physiological responses, and that one must take care to eliminate such confusing behaviors if one is using physiological measurement as a proxy for subjective experience in a VE.
Design of Communication Systems Using Deep Learning: A Variational Inference Perspective

Recent research in the design of end to end communication system using deep learning has produced models which can outperform traditional communication schemes. Most of these architectures leveraged autoencoders to design the encoder at the transmitter and decoder at the receiver and train them jointly by modeling transmit symbols as latent codes from the encoder. However, in communication systems, the receiver has to work with noise corrupted versions of transmit symbols. Traditional autoencoders are not designed to work with latent codes corrupted with noise. In this work, we provide a framework to design end to end communication systems which accounts for the existence of noise corrupted transmit symbols. The proposed method uses deep neural architecture. An objective function for optimizing these models is derived based on the concepts of variational inference. Further, domain knowledge such as channel type can be systematically integrated into the objective. Through numerical simulation, the proposed method is shown to consistently produce models with better packing density and achieving it faster in multiple popular channel models as compared to the previous works leveraging deep learning models.
A supervised deep convolutional based bidirectional long short term memory video hashing for large scale video retrieval applications

Recently, large scale video content retrieval has gained more attention due to a large amount of user-generated images and video content available over the internet. Hashing is one of the effective techniques that encode the high dimensional features vectors into compact binary codes. The aim of hashing is to generate the short binary codes and map the similar hash code values to retrieve similar video from the database with minimum distance measure. Deep learning-based hashing networks are employed to learn the representative video feature to estimate the hash functions. However, the existing hashing approaches fail due to the frame-level feature representation and does not well exploits the effective temporal features in visual search. Furthermore, significant loss of features during the dimensionality reduction step causes low accuracy. Hence, it is essential to develop a deep learning-based hashing framework that should exploits both the strong spatial and temporal features for the scalable video search. The main objective is to learn the high dimensional features from the entire video and derive compact binary codes to retrieve the similar videos for the input query sequence. In this paper, we propose a joint network model of supervised stacked heterogeneous convolutional multi-kernel (Stacked HetConv-MK)-bidirectional Long Short Term Memory (BiDLSTM) network model that effectively encodes the rich structural as well as the discriminative features from the video sequence to estimate the compact binary codes. Initially, the video frames are passed to the stacked convolution networks with heterogeneous convolutional kernel size and residual learning to extract the spatial features at different views from the video sequences and to improve the learning efficiency. Then, the bidirectional network computes the sequence in both forward and backward directions and obtains the series of hidden state output. Finally, the fully connected structure with an activation unit performs hashing to learn the multiple codes for each video. Experimental analysis is performed on three datasets and the result shows a better accuracy measure than the other state-of-art approaches.