Reviewerly/exHarmony huggingface dataset:
ARC Containers for AI Workloads

Containerization has taken the software world by storm. Deployment complications, like requiring elevated (i.e. root) permissions to run, have slowed the adoption of containers in shared advanced research computing (ARC) environments. Singularity is a containerization approach that is designed for ARC in shared high performance computing (HPC) clusters. With the creation of the Singularity, there is finally a viable scientific container solution. However very few papers have looked at the performance trade-offs of deploying applications using a container based model. The authors are not aware of any published studies evaluating the trade-offs of the deployment models with complex Artificial Intelligence (AI) workloads. Without detailed evaluations of the performance trade-offs scientists and engineers are unable to make an informed decision on deployment model for time sensitive training or low power inference. Furthering previous research in this area and using emerging community developed benchmarks, we examine performance trade-offs of running AI workloads in a containerized Singularity environment.
An Efficient Greedy Algorithm for Sequence Recommendation

Recommending a sequence of items that maximizes some objective function arises in many real-world applications. In this paper, we consider a utility function over sequences of items where sequential dependencies between items are modeled using a directed graph. We propose EdGe, an efficient greedy algorithm for this problem and we demonstrate its effectiveness on both synthetic and real datasets. We show that EdGe achieves comparable recommendation precision to the state-of-the-art related work OMEGA, and in considerably less time. This work opens several new directions that we discuss at the end of the paper.
Pairing: Privately Balancing Multiparty Real-Time Supply and Demand on the Power Grid

Microgrids equipped with renewable energy resources have proven to be critical building blocks on the power grid that can greatly improve the grid performance. A promising application would be enabling microgrids to utilize their local energy for further balancing the regional supply and demand at different times - ensuring better system economics and reliability. However, due to the privacy concerns on continuously revealing each microgrid's local data for deriving real-time optimal balancing decisions, the application of such promising cooperative technique is still limited. In this paper, we design an efficient cryptographic protocol for privately balancing the regional supply and demand, as well as each microgrid's local supply and demand in real time. We prove the security of our protocol against both passive and active adversaries. Meanwhile, we implemented a prototype of the Pairing system that integrates cryptographic protocol and the power transmission network. We mount the real smart grid datasets into Pairing in real time for system evaluations. The experimental results demonstrate the practicality of our system by scaling to hundreds of microgrids with high accuracy and efficient system performance.
Avatars for Co-located Collaborations in HMO-based Virtual Environments

Multi-user virtual reality is transforming towards a social activity that is no longer only used by remote users, but also in large-scale location-based experiences. Usage of realtime-tracked avatars in colocated business-oriented applications with a “guide-user-scenario” is examined for user-related factors of Spatial Presence, Social Presence, User Experience and Task Load. A user study was conducted in order to compare both techniques of a realtime-tracked avatar and a non-visualised guide. Results reveal that the avatar-guide enhanced and stimulated communicative processes while facilitating interaction possibilities and creating a higher sense of mental immersion for users and engagement.
The Sitting Posture Monitoring Method Based on Notch Sensor

Being in bad sitting posture for a long time will cause serious injury to human body, such as myopia, humpback, cervical spine diseases. This paper classifies sitting posture according to medical research, and collects data from the sitting posture of sedentary people in daily work and study based on Notch sensor wearable device and Android smartphone. By extracting 8 characteristic values such as euler Angle and axis data, a sitting posture classification model based on threshold value and k-nearest neighbor algorithm was established to realize real-time monitoring and sitting posture analysis of the sedentary people. Experiments show that the system can identify poor sitting posture with 98 percents accuracy, providing a scientific basis for improving the sub-health caused by poor sitting posture.
Local outlier detection based on information entropy weighting

As a key research area in data mining technologies, outlier detection can expose data inconsistent with the majority in the dataset and therefore is applicable in extensive areas. The addition of entropy weighting to the spatial local outlier measure (SLOM) and local distance-based outlier factor (LDOF) algorithms in outlier data mining, i.e., the adoption of entropy in the calculation of weighted distance is taken into consideration, leads to enhanced accuracy of outlier detection and produces more expense of time. The algorithm of entropy-weighted LDOF is more optimised than that of entropy-weighted SLOM in terms of detection accuracy. The superiority of the entropy-weighted algorithm is verified through experimental results.
Artificial bee colony algorithm for feature selection and improved support vector machine for text classification

Purpose Owing to the huge volume of documents available on the internet, text classification becomes a necessary task to handle these documents. To achieve optimal text classification results, feature selection, an important stage, is used to curtail the dimensionality of text documents by choosing suitable features. The main purpose of this research work is to classify the personal computer documents based on their content. Design/methodology/approach This paper proposes a new algorithm for feature selection based on artificial bee colony (ABCFS) to enhance the text classification accuracy. The proposed algorithm (ABCFS) is scrutinized with the real and benchmark data sets, which is contrary to the other existing feature selection approaches such as information gain and χ 2 statistic. To justify the efficiency of the proposed algorithm, the support vector machine (SVM) and improved SVM classifier are used in this paper. Findings The experiment was conducted on real and benchmark data sets. The real data set was collected in the form of documents that were stored in the personal computer, and the benchmark data set was collected from Reuters and 20 Newsgroups corpus. The results prove the performance of the proposed feature selection algorithm by enhancing the text document classification accuracy. Originality/value This paper proposes a new ABCFS algorithm for feature selection, evaluates the efficiency of the ABCFS algorithm and improves the support vector machine. In this paper, the ABCFS algorithm is used to select the features from text (unstructured) documents. Although, there is no text feature selection algorithm in the existing work, the ABCFS algorithm is used to select the data (structured) features. The proposed algorithm will classify the documents automatically based on their content.
Multiclass heterogeneous domain adaptation via bidirectional ECOC projection

Heterogeneous domain adaptation aims to exploit the source domain data to train a prediction model for the target domain with different input feature space. Current methods either map the data points from different domains with different feature space to a common latent subspace or use asymmetric projections for learning the classifier. However, these learning methods separate common space learning and shared classifier training. This may lead complex model structure and more parameters to be determined. To appropriately address this problem, we propose a novel bidirectional ECOC projection method, named HDA-ECOC, for heterogeneous domain adaptation. The proposed method projects the inputs and outputs (labels) of two domains into a common ECOC coding space, such that, the common space learning and the shared classifier training can be performed simultaneously. Then, classification of the target testing sample can be directly addressed by an ECOC decoding. Moreover, the unlabeled target data is exploited by estimating the two domains projected instances consistency through a maximum mean discrepancy (MMD) criterion. We formulate this method as a dual convex minimization problem and propose an alternating optimization algorithm for solving it. For performance evaluation, experiments are performed on cross-lingual text classification and cross-domain digital image classification with heterogeneous feature space. The experimental results demonstrate that the proposed method is effective and efficient in solving the heterogeneous domain adaptation problems.
BMI Prediction From Face Images

Body-mass index (BMI) is the amount of mass per area of a person, and it is an important indicator of the weight status. From health industry till the social media applications, there are many areas where BMI data is used. Various machine learning techniques are developed for BMI prediction only from a face image without any information about the weight and height of a person.Making this kind of predictions is a regression problem. In this study, a deep network based BMI predictor tool is designed and its performance is compared with the existing methods from previous studies. Additionally, a new data set for validation purposes is introduced.
Evaluation of Human Body Detection Using Deep Neural Networks with Highly Compressed Videos for UAV Search and Rescue Missions

Dealing with compressed video streams in mobile robotics is an unavoidable fact of life. Transferring images between mobile robots or to the Cloud using wireless links can practically only be achieved using lossy video compression. This introduces artifacts that often make image processing challenging. Recent algorithms based on deep neural networks, as advanced as they are, are commonly trained and evaluated on datasets of high-fidelity images which are typically not captured from aerial views. In this work we evaluate a number of deep neural network based object detection algorithms in the context of aerial search and rescue scenarios where real-time and robust detection of human bodies is a priority. We provide an evaluation using a number of video sequences collected in-flight using Unmanned Aerial Vehicle (UAV) platforms in different environmental conditions. We also describe the detection performance degradation under limited bitrate compression using H.264, H.265 and VP9 video codecs, in addition to analyzing the timing effects of moving image processing tasks to off-board entities.
Deep Learning Based Abstractive Turkish News Summarization

With the increase of knowledge, there is a need for summarization systems that will direct the person to the area they are interested in without any waste of time. In this work, Turkish news headlines have been predicted by using encoder-decoder model from deep learning methods. Abstraction based text summarization method has been used during the generation of headlines. The system has been trained with recurrent neural networks by developing encoder-decoder model. The word embeddings of the words in news texts have been generated by using FastText that is very commonly used model in the literature recently. The system has been tested separately by training the first sentence, first two sentences and full-text of each news. The success of the system is measured by ROUGE score and semantic similarity score. According to the experimental results, it has been observed the model trained with full-text of news outperforms among the other models.
Rapid Estimation of the Epicentral Distance in the Earthquake Early Warning System around the Tehran Region, Iran

ABSTRACT The estimation of epicentral distance is a critical step in earthquake early warning systems (EEWSs) that is necessary to characterize the level of expected ground shaking. In this study, two rapid methodologies, that is, B‐Δ and C‐Δ, are evaluated to estimate the epicentral distance for use in the EEWSs around the Tehran region. Traditionally, the B and C coefficients are computed using acceleration records, however, in this study, we utilize both acceleration and velocity waveforms for obtaining a suitable B‐Δ and C‐Δ relationships for the Tehran region. In comparison with observations from Japan, our measurements fall within the range of scatter. However, our results show a lower trend, which can strongly depend on the few numbers of events and range of magnitude (small‐to‐moderate) of earthquakes used in the current research. To improve our result, we include some large earthquakes from Iran, Italy, and Japan with magnitude larger than 5.9. Although the optimal trend is finally obtained by fitting a line to the distance‐averaged points, we conclude that the same trend and relationship as Japan can be used in Tehran early warning system. We also found that B and C parameters are strongly compatible to each other. As time windows of 3.0 and 0.5 s after the P onset are chosen respectively to compute the B and C values, so by selecting the C parameter as a proxy of B parameter to estimate the epicentral distance, we may save significant time in order of about 2.5 s in any earthquake early warning applications.
Distributed Ledger for Spammers' Resume

Unsolicited, and most likely spoofed, robot calls are not just an annoyance, but also carry a potential threat with the onset of automation, impersonation, and even voice manipulation technologies as malicious elements attempt to use deception to steal sensitive information or invoke action. Despite steps taken to protect consumers, the issue appears to be far from under control. In this paper, we propose a solution to use blockchain as a platform to share spam transactions through a peer-to-peer mechanism that will maintain a global database of reported spam transactions in order to identify and trace spam activity effectively. Storing spam transactions on a distributed ledger with consensus-based approval of transactions adds reliability to the data and can optimize the data points that will be available to spam detection algorithms in order to fight spam effectively. As this is peer to peer-based sharing, there is no need to rely on thirdparty providers for storing and sharing this data to the users. Every spam call received will be added as a detailed transaction on the blockchain to execute a smart contract that will calculate the trustworthiness of the caller. Call records are used to identify spam transactions while the blockchain ledgers store this data. We discuss the relevance and advantages of a distributed ledger to store these transactions. This paper does not aim at solving the spam problem with an optimized detection algorithm but evaluates the characteristics and performance of the blockchain as a distributed ledger and its relevance to serve as a platform for peer-to-peer spam detection mechanisms. We evaluate different blockchain metrics like transaction processing rates, gas costs and ledger sizes and discuss how they scale in order to store the spam reports data on the blockchain.
MSVD-Turkish: A Large-Scale Dataset for Video Captioning in Turkish

Automatically generating natural language descriptions for videos, aka video captioning, has been recently introduced as a challenging integrated vision and language problem. Although researchers have demonstrated numerous solutions for English, to date there has been no study on Turkish language due to the lack of suitable datasets to train Turkish video captioning models. To tackle this, in this study we construct a largescale Turkish benchmark dataset by carefully translating English descriptions from MSVD dataset to Turkish. Moreover, we implement several neural models, including LSTM-based sequence-to-sequence architectures with temporal attention mechanisms, and report the performances of these strong baselines on our dataset. We hope that our dataset will serve as a good resource for future efforts on Turkish video captioning.
Blockchain and cryptocurrencies: a cross-border conundrum

Purpose To analyze the CFTC’s approach to regulating cryptocurrencies and blockchain technologies in light of their cross-border nature, limitations on the CFTC’s extraterritorial authority, and the CFTC’s prerogative to work cooperatively with foreign regulators. Design/methodology/approach Discusses the principles set forth in CFTC Chairman Christopher Giancarlo’s White Paper regarding cross-border swap regulation; analyzes the similar nature of cross-border issues arising from regulation of cryptocurrencies and blockchain technologies; examines regulations and guidance implemented by foreign authorities in the blockchain and cryptocurrency space; and assesses the limitations of the CFTC’s extraterritorial authority. Findings The principles set forth in Chairman Giancarlo’s White Paper regarding cross-border swap regulation apply equally to blockchain technologies and cryptocurrencies, and as such, the CFTC may wish to pursue an analogous approach to regulating cryptocurrencies and blockchain technologies. Practical implications The CFTC should exercise deference to and cooperate with foreign counterparts to regulate cryptocurrencies and blockchain technologies that traverse international borders, thereby avoiding overlapping and potentially conflicting regulation while fostering an innovative growth environment for emerging technologies. Originality/value In-depth analysis and insight from experienced professionals in the CFTC and cross-border investigations and enforcement space.
Investigating Screen Reachability on an Articulated Dual-Display Smartphone

Large displays on smartphones accommodate tasks needing more screen real-estate, but at the expense of limiting the use of one-handed operations. In this paper we explore thumb reachability on an articulated dual-display smartphone. While mobile devices are featuring dual screens, no previous work has explored how each physical display’s positions impact one-handed reach and interaction. We explore 32 inter-display configurations resulting from the combination of orientations of the two displays along two axes. In a preliminary study we explore how users grasp such dual-display configurations. We then conduct a study to investigate the effects of the relative position of two displays on thumb reachability. Results provide a range of dual-display configurations that are ideal for one-handed use, enabling knowledge on how apps for such emerging devices can be optimized for thumb input.
An Efficient Anaglyph 3D Video Watermarking Approach Based on Hybrid Insertion

Digital watermarking techniques have been proposed as an efficient solution to protect different media from illegal manipulations. However, for 3D videos, this domain has not been developed enough where only three watermarking methods exist for anaglyph 3D videos. These existing methods suffer from robustness insufficiency, especially against malicious attacks such as video compression. In this paper, a robust hybrid anaglyph 3D video watermarking algorithm, which is based on spatial and frequency domains (Least Significant Bit (LSB), Discrete Wavelet Transform (DWT) and Discrete Cosines Transform (DCT)) is proposed. First, the original anaglyph 3D video is divided into a set of frames, then each frame is split into cyan and red images. A first signature will be inserted in cyan and red images using, respectively, DCT and LSB based techniques. Second, the obtained anaglyph frame is processed by the first level of the DWT, where the second signature is inserted in a low frequency. The inverse of a DWT is performed to obtain the final marked anaglyph frame. The proposed approach is evaluated based on invisibility and robustness criteria and shows a high level of invisibility and robustness against different video compression standards such as MPEG-4 and H264-AVC.
Metadata-private communication for the 99%

Sketching the underlying system needed to facilitate metadata-private communication for several applications with a large user base.
Mining GOLD Samples for Conditional GANs

Conditional generative adversarial networks (cGANs) have gained a considerable attention in recent years due to its class-wise controllability and superior quality for complex generation tasks. We introduce a simple yet effective approach to improving cGANs by measuring the discrepancy between the data distribution and the model distribution on given samples. The proposed measure, coined the gap of log-densities (GOLD), provides an effective self-diagnosis for cGANs while being efficiently, computed from the discriminator. We propose three applications of the GOLD: example re-weighting, rejection sampling, and active learning, which improve the training, inference, and data selection of cGANs, respectively. Our experimental results demonstrate that the proposed methods outperform corresponding baselines for all three applications on different image datasets.
KerGM: Kernelized Graph Matching

Graph matching plays a central role in such fields as computer vision, pattern recognition, and bioinformatics. Graph matching problems can be cast as two types of quadratic assignment problems (QAPs): Koopmans-Beckmann's QAP or Lawler's QAP. In our paper, we provide a unifying view for these two problems by introducing new rules for array operations in Hilbert spaces. Consequently, Lawler's QAP can be considered as the Koopmans-Beckmann's alignment between two arrays in reproducing kernel Hilbert spaces (RKHS), making it possible to efficiently solve the problem without computing a huge affinity matrix. Furthermore, we develop the entropy-regularized Frank-Wolfe (EnFW) algorithm for optimizing QAPs, which has the same convergence rate as the original FW algorithm while dramatically reducing the computational burden for each outer iteration. We conduct extensive experiments to evaluate our approach, and show that our algorithm significantly outperforms the state-of-the-art in both matching accuracy and scalability.
Efficiently avoiding saddle points with zero order methods: No gradients required

We consider the case of derivative-free algorithms for non-convex optimization, also known as zero order algorithms, that use only function evaluations rather than gradients. For a wide variety of gradient approximators based on finite differences, we establish asymptotic convergence to second order stationary points using a carefully tailored application of the Stable Manifold Theorem. Regarding efficiency, we introduce a noisy zero-order method that converges to second order stationary points, i.e avoids saddle points. Our algorithm uses only $\tilde{\mathcal{O}}(1 / \epsilon^2)$ approximate gradient calculations and, thus, it matches the converge rate guarantees of their exact gradient counterparts up to constants. In contrast to previous work, our convergence rate analysis avoids imposing additional dimension dependent slowdowns in the number of iterations required for non-convex zero order optimization.