Reviewerly/exHarmony huggingface dataset:
Recurrent Positional Embedding for Neural Machine Translation

In the Transformer network architecture, positional embeddings are used to encode order dependencies into the input representation.However, this input representation only involves static order dependencies based on discrete numerical information, that is, are independent of word content.To address this issue, this work proposes a recurrent positional embedding approach based on word vector.In this approach, these recurrent positional embeddings are learned by a recurrent neural network, encoding word content-based order dependencies into the input representation.They are then integrated into the existing multi-head selfattention model as independent heads or part of each head.The experimental results revealed that the proposed approach improved translation performance over that of the stateof-the-art Transformer baseline in WMT'14 English-to-German and NIST Chinese-to-English translation tasks.
Guided, Fusion-Based, Large Depth-of-field 3D Imaging Using a Focal Stack

Three dimensional (3D) imaging technology has been widely used for many applications, such as human-computer interactions, making industrial measurements, and dealing with cultural relics. However, existing active methods often require both large apertures of projector and camera to maximize light throughput, resulting in a shallow working volume in which projector and camera are simultaneously in focus. In this paper, we propose a novel method to extend the working range of the structured light 3D imaging system based on the focal stack. Specifically in the case of large depth variation scenes, we first adopted the gray code method for local, 3D shape measurement with multiple focal distance settings. Then we extracted the texture map of each focus position into a focal stack to generate a global coarse depth map. Under the guidance of the global coarse depth map, the high-quality 3D shape measurement of the overall scene was obtained by local, 3D shape-measurement fusion. To validate the method, we developed a prototype system that can perform high-quality measurements in the depth range of 400 mm with a measurement error of 0.08%.
A Quality of Experience Evaluation Comparing Augmented Reality and Paper Based Instruction for Complex Task Assistance

Augmented reality (AR) can support a user in performing an expert task by overlaying real world objects with the domain specific information required to complete the task. Understanding how users can process and use such information is very important for informing the design of AR technologies and applications. In this paper, the results of a quality of experience (QoE) evaluation of an AR application for the task of solving a Rubik's Cube are presented. The Rubik's Cube was selected based on its familiarity and the expertise needed to solve it unaided. An empirical approach was taken to identify the QoE features that affect the usability and utility of an AR head-mounted display (HMD) compared with paper-based instruction. The QoE evaluation methodology involved the capture and analysis of implicit and explicit QoE metrics. The utility (in terms of performance) of each mode of instruction was objectively measured using: (a) cube completion success rates; and (b) time-to-completion. The implicit metrics of electrodermal activity (EDA), skin temperature, heart rate and the novel use of facial action units (AUs) were recorded to infer emotional state during the task completion. Finally, with respect to explicit metrics, the test subjects completed a Likert scale questionnaire post the experience to subjectively report QoE as well as a self-assessment manikin (SAM) questionnaire to self-report emotional state upon task completion. The results show that AR yielded higher success rates and significantly lower time-to-completion rates. The AR group explicitly reported higher levels of positive valance (affective state) than the paper-based group. The physiological data showed that the AR group were less stressed (via EDA) than the paper-based group. Finally, analysis of the AU data reflected a greater than chance (total: 21.85%) accuracy when predicting affective state based on SAM questionnaires as ground-truth.
Temporal Feature Augmented Network for Video Instance Segmentation

In this paper, we propose a temporal feature augmented network for video instance segmentation. Video instance segmentation task can be split into two subtasks: instance segmentation and tracking. Similar to the previous work, a track head is added to an instance segmentation network to track object instances across frames. Then the network can performing detection, segmentation and tracking tasks simultaneously. We choose the Cascade-RCNN as the basic instance segmentation network. Besides, in order to make better use of the rich information contained in the video, a temporal feature augmented module is introduced to the network. When performing instance segmentation task on a single frame, information from other frames in the same video will be included and the performance of instance segmentation task can be effectively improved. Moreover, experiments show that the temporal feature augmented module can effectively alleviate the problem of motion blur and pose variation.
End-to-End-Based Tibetan Multitask Speech Recognition

To date, speech recognition technology for majority languages has been applied in wireless communication devices successfully. However, as a minority language, Tibetan has very limited resources for conventional automatic speech recognition. It lacks of enough data, sub-word units, lexicons, and word inventories for some dialects. In this paper, we present a multitask end-to-end model to perform simultaneous Tibetan speech content recognition, dialect identification, and speaker recognition. This model avoids processing the pronunciation dictionary and word segmentation for new dialects while allowing for training three tasks in a single model. We build the multitask recognition framework based on WaveNet-CTC. The dialect information and speaker ID are used in the output for training. The experimental results show that our method has better performance compared with a task-specific model.
Energy Efficient Lightweight Cryptography Algorithms for IoT Devices

Over few decades, people have been working on providing security solutions, whereas attackers too have been working simultaneously. We present an evaluation of security algorithms, comparing performances and robustness. These comparisons are performed after hardware implementation and use crypt analysis. The targeted devices are wrist watches, RFID tags, IoT devices and others, which don't have a lot of areas (millions of gate equivalent). While performing this, the primary concern has been exploration of an algorithm that can work in these constrained limits. This led to a search for an algorithm that has low hardware footprints, low power consumption and better speed but at the same time implements adequate security. PRESENT has been found to be one such suitable algorithm. It has been also included in the new international standard for light-weight cryptographic methods under ISO/IEC 29192-2:2012 for its straight forward and light design. Our paper reports hardware implementation results of PRESENT, AES, ECDH, DH and RSA cryptography algorithms. We have implemented these algorithms with standard gate library of UMC-90 nm. Each algorithm has its own architecture and hence requires different crypt-analysis techniques like brute force, Pollard's Rho and biclique for “difficulty to break” measurement. It is a measure in term of time and data complexity of efforts required of a cryptographic attack. We have obtained 1.7× improvement in area and 63× improvement in power for modified PRESENT algorithm as compared to AES. It has also been observed that the proposed PRESENT algorithm has a time complexity of break attack as 2127 for 128 bit key length.
Transferable heterogeneous feature subspace learning for JPEG mismatched steganalysis

Steganalysis is a technique that detects the presence of secret information in multimedia data. Many steganalysis algorithms have been proposed with high detection accuracy; however, the difference in statistical distribution between training and testing sets can cause mismatch problems, which will degrade the performance of traditional steganalysis algorithms. To solve this problem, we propose a transferable heterogeneous feature subspace learning (THFSL) algorithm for JPEG mismatched steganalysis. Our approach considers the feature space in each domain as a combination of the domain-independent features and the domain-related features. We use the transformation matrix to transfer both the domain-independent and domain-related features from the source and target domains to a common feature subspace, where each target sample can be better represented by a combination of source samples. By imposing low-rank constraints on the domain-independent features, the structures of data can be preserved, which can capture the intrinsic structures for discriminating cover and stego images. Our method can avoid a potentially negative transfer by using a sparse matrix to model the domain-related features and, thus, is more robust to different domain changes in mismatched steganalysis. Extensive experiments on various mismatched steganalysis tasks show the superiority of the proposed method over the state-of-the art methods.
Emotional State Prediction From Online Handwriting and Signature Biometrics

Handwriting and signature biometrics have a long history in the literature, especially in terms of identity recognition and/or verification; nevertheless, it reveals more information therefore provides more opportunities for personal characteristics estimation, particularly, emotional state.However, almost all publicly or commercially available databases do not include contributors' demographic labels or emotional status in addition to their identification labels, leading these available databases to be only useful for verification and identification based research studies.For this reason, this paper proposes both offline and online handwriting and signature biometric database with a wide range of ground truths (emotional status labels -happy, sad and stress) in addition to the identity labels and tries to predict ones' emotional state, namely happy, sad and stress, from their online biometric handwriting and signatures.The proposed database comprised total of 134 participants with 804 handwriting and 8040 signature biometric samples.The database presented also includes individuals' demographic information such as age, gender, handedness, education level and nationality.Subsequently, there are several experiments have been conducted, with different thresholds, which present the usability of the proposed database and preliminary results of emotional state prediction from both signature and handwriting biometrics.The experiments achieved remarkable success especially on stress prediction for handwriting.On the other hand, considering the results from signature biometrics, it is observed that happy and then sad and finally stress class forms the most part of the prediction accuracy.
TRB: A Novel Triplet Representation for Understanding 2D Human Body

Human pose and shape are two important components of 2D human body. However, how to efficiently represent both of them in images is still an open question. In this paper, we propose the Triplet Representation for Body (TRB) --- a compact 2D human body representation, with skeleton keypoints capturing human pose information and contour keypoints containing human shape information. TRB not only preserves the flexibility of skeleton keypoint representation, but also contains rich pose and human shape information. Therefore, it promises broader application areas, such as human shape editing and conditional image generation. We further introduce the challenging problem of TRB estimation, where joint learning of human pose and shape is required. We construct several large-scale TRB estimation datasets, based on the popular 2D pose datasets LSP, MPII and COCO. To effectively solve TRB estimation, we propose a two-branch network (TRB-net) with three novel techniques, namely X-structure (Xs), Directional Convolution (DC) and Pairwise mapping (PM), to enforce multi-level message passing for joint feature learning. We evaluate our proposed TRB-net and several leading approaches on our proposed TRB datasets, and demonstrate the superiority of our method through extensive evaluations.
Mathematics learning instrument using augmented reality for learning 3D geometry

Abstract Augmented Reality (AR) is one kind of Virtual Reality (VR) technologies that can be used to help students who have difficulties to imagine geometrical objects in three-dimension way. AR can be applied in mobile learning which will improve the efficiency and effectiveness in learning process. The aim of this study was to develop the mathematics learning instruments using AR for secondary students. The methodology that used in this study was research and development. Sketchup software is used to draw geometrical objects and AR application can be used to view the objects in 3D. The subjects of this study were 15 students of grade nine. Experts’ assessments and students’ responses results were used to collect the data. The finding shows that the instrument is categorized as valid and it can be implemented in learning process. The validity aspects consist of languages, contents, and design. Besides, the instrument can be used in learning process, catch students’ interest and help them to understand geometry contents.
Correcting for Recency Bias in Job Recommendation

Users are known to interact more with fresh content in certain temporally associated domains such as news search or job seeking, leading to an uneven distribution of interactions over items of different degrees of freshness. Data collected under such an "aging effect'' is usually used unconditionally on all sort of recommendation tasks, and as a result more recently published content may be over-represented during model training and evaluation. In this study, we characterize this temporal influence as a recency bias, and present an analysis in the domain of job recommendation. We show that, by correcting for recency bias using an unbiased learning to rank approach, one can improve the quality of recommendation significantly over a recent neural collaborative filtering model on RecSys Challenge 2017 data.
Multi-Turn Response Selection in Retrieval-Based Chatbots with Iterated Attentive Convolution Matching Network

Building an intelligent chatbot with multi-turn dialogue ability is a major challenge, which requires understanding the multi-view semantic and dependency correlation among words, n-grams and sub-sequences. In this paper, we investigate selecting the proper response for a context through multi-grained representation and interactive matching. To construct hierarchical representation types of text segments, we propose a refined architecture which exclusively consists of gated dilated-convolution and self-attention. Compared with the recurrent-based sentence modeling methods, this architecture provides more flexibility and a speedup. The matching signals of each utterance-response pair are extracted by integrating the interactive information from different views. Then a turns-aware attention mechanism is utilized to aggregate the matching sequence, so as to identify important utterances and capture the implicit relationship of the whole context. Experiments on two large-scale public data sets show that our model significantly outperforms the state-of-the-art methods in terms of all metrics. We empirically provide a thorough ablation test, as well as the comparison of different representation and matching strategies, for a better insight into how each component affects the performance of the model.
Understanding the Knowledge Gaps of Software Engineers

Context: Knowledge level and productivity of the software engineering (SE) workforce are the subject of regular discussions among practitioners, educators, and researchers. There have been many efforts to measure and improve the knowledge gap between SE education and industrial needs. Objective: Although the existing efforts for aligning SE education and industrial needs have provided valuable insights, there is a need for analyzing the SE topics in a more “fine-grained” manner; i.e., knowing that SE university graduates should know more about requirements engineering is important, but it is more valuable to know the exact topics of requirements engineering that are most important in the industry. Method: We achieve the above objective by assessing the knowledge gaps of software engineers by designing and executing an opinion survey on levels of knowledge learned in universities versus skills needed in industry. We designed the survey by using the SE knowledge areas (KAs) from the latest version of the Software Engineering Body of Knowledge (SWEBOK v3), which classifies the SE knowledge into 12 KAs, which are themselves broken down into 67 subareas (sub-KAs) in total. Our analysis is based on (opinion) data gathered from 129 practitioners, who are mostly based in Turkey. Results: Based on our findings, we recommend that educators should include more materials on software maintenance, software configuration management, and testing in their SE curriculum. Based on the literature as well as the current trends in industry, we provide actionable suggestions to improve SE curriculum to decrease the knowledge gap.
Feature Selection Based on Swallow Swarm Optimization for Fuzzy Classification

This paper concerns several important topics of the Symmetry journal, namely, pattern recognition, computer-aided design, diversity and similarity. We also take advantage of the symmetric structure of a membership function. Searching for the (sub) optimal subset of features is an NP-hard problem. In this paper, a binary swallow swarm optimization (BSSO) algorithm for feature selection is proposed. To solve the classification problem, we use a fuzzy rule-based classifier. To evaluate the feature selection performance of our method, BSSO is compared to induction without feature selection and some similar algorithms on well-known benchmark datasets. Experimental results show the promising behavior of the proposed method in the optimal selection of features.
Deep autoencoders for attribute preserving face de-identification

The mass availability of mobile devices equipped with cameras has lead to increased public privacy concerns in recent years. Face de-identification is a necessary first step towards anonymity preservation, and can be trivially solved by blurring or concealing detected faces. However, such naive privacy protection methods are both ineffective and unsatisfying, producing a visually unpleasant result. In this paper, we tackle face de-identification using Deep Autoencoders, by finetuning the encoder to perform face de-identification. We present various methods to finetune the encoder in both a supervised and unsupervised fashion to preserve facial attributes, while generating new faces which are both visually and quantitatively different from the original ones. Furthermore, we quantify the realism and naturalness of the resulting faces by introducing a diversity metric to measure the distinctiveness of the new faces. Experimental results show that the proposed methods can generate new faces with different person identity labels, while maintaining the facelike nature and diversity of the input face images.
Using Visualization Science to Improve Expert and Public Understanding of Probabilistic Temperature and Precipitation Outlooks

Abstract Visually communicating temperature and precipitation climate outlook graphics is challenging because it requires the viewer to be familiar with probabilities as well as to have the visual literacy to interpret geospatial forecast uncertainty. In addition, the visualization scientific literature has open questions on which visual design choices are the most effective at expressing the multidimensionality of uncertain forecasts, leaving designers with a lack of concrete guidance. Using a two-phase experimental setup, this study shows how recently developed visualization diagnostic guidelines can be used to iteratively diagnose, redesign, and test the understandability the U.S. National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Center (CPC) climate outlooks. In the first phase, visualization diagnostic guidelines were used in conjunction with interviews and focus groups to identify understandability challenges of existing visual conventions in temperature and precipitation outlooks. Next, in a randomized control versus experimental treatment setup, several graphic modifications were produced and tested via an online survey of end users and the general public. Results show that, overall, end users exhibit a better understanding of outlooks, but some types of probabilistic color mapping are misunderstood by both end users and the general public, which was predicted by the diagnostic guidelines. Modifications lead to significant gains in end-user and general public understanding of climate outlooks, providing additional evidence for the utility of using control versus treatment testing informed by visualization diagnostics.
An Experimental Study of Software Aging and Rejuvenation in Dockerd

Virtualized containers are being extensively used to host applications as they substantially reduce the overhead caused by conventional virtualization techniques. Therefore, as containers adoption grows, the need for dependability also increases. Dockerd, the process that is in charge of Docker containers management, is supposed to support long-running systems, which makes it prone to the well-known problem of software aging. This paper presents an experimental study of software aging and rejuvenation targeting the dockerd daemon. We used the SWARE approach to conduct the experimentation, which encompasses three phases: i) stress - stress environment with the accelerated workload to induce bugs activation; ii) wait - stop the workload submission to observe possible accumulated effects and; iii) rejuvenation - submit a rejuvenation action to perceive changes in the internal software state. The experiment runs for 26 days, and results show that dockerd suffers from software aging effects after the stress phase. The accumulated effects remain in the system until a complete cleanup, comprising removing all the containers and rebooting the operating system.
Securely Sampling Biased Coins with Applications to Differential Privacy

We design an efficient method for sampling a large batch of d independent coins with a given bias p ∈ [0,1]. The folklore secure computation method for doing so requires O(lambda + log d) communication and computation per coin to achieve total statistical difference 2-lambda. We present an exponential improvement over the folklore method that uses just O(log(lambda+log d)) gates per coin when sampling d coins with total statistical difference 2-lambda. We present a variant of our work that also concretely beats the folklore method for lambda ≥ 60 which are parameters that are often used in practice. Our new technique relies on using specially designed oblivious data structures to achieve biased coin samples that take an expected 2 random bits to sample. Using our new sampling technique, we present an implementation of the differentially private report-noisy-max mechanism (a more practical implementation of the celebrated exponential mechanism) as a secure multi-party computation. Our benchmarks show that one can run this mechanism on a domain of size d=212 in 6 seconds and up to d=219 in 14 minutes. As far as we know, this is the first complete distributed implementation of either of these mechanisms.