Reviewerly/exHarmony huggingface dataset:
A Constant-Time Algorithm for Middle Levels Gray Codes

For any integer $$n\ge 1$$, a middle levels Gray code is a cyclic listing of all n-element and $$(n+1)$$-element subsets of $$\{1,2,\ldots ,2n+1\}$$ such that any two consecutive sets differ in adding or removing a single element. The question whether such a Gray code exists for any $$n\ge 1$$ has been the subject of intensive research during the last 30 years, and has been answered affirmatively only recently [T. Mütze. Proof of the middle levels conjecture. Proc. London Math. Soc., 112(4):677–713, 2016]. In a follow-up paper [T. Mütze and J. Nummenpalo. An efficient algorithm for computing a middle levels Gray code. ACM Trans. Algorithms, 14(2):29 pp., 2018] this existence proof was turned into an algorithm that computes each new set in the Gray code in time $${{\mathcal {O}}}(n)$$ on average. In this work we present an algorithm for computing a middle levels Gray code in optimal time and space: each new set is generated in time $${{\mathcal {O}}}(1)$$ on average, and the required space is $${{\mathcal {O}}}(n)$$.
Statistical Appliance Inference in the Smart Grid by Machine Learning

Smart Grid has been attracting more interest than ever thanks to emergence of enabling technologies such as 5G and IoT. Yet, there are some long-standing privacy concerns about revealing habits and lifestyles of people from fine-grained power consumption data collected through smart meters. In this context, the contribution of this work is twofold: First, we empirically demonstrate how appliance-level fine-grained power consumption data can reveal households' routines simply using probability density estimations derived from consumption data without requiring any complex analysis. Second, we point out that appliance types can be identified in a targeted house using circuit-level consumption data of other houses. We show how machine learning can be used maliciously to realize this threat in an automatic manner and achieve high success rate even with limited amount of training data on the public REDD dataset. In addition, we provide discussions on possible countermeasures against the threats examined in this study.
DTC: Transfer learning for commonsense machine comprehension

Commonsense Machine Comprehension (CMC) is a popular natural language understanding task. CMC enables computers to learn about causal and temporal reasoning by exploiting implicit commonsense knowledge and can be applied to Question Answering, Search Engine and Dialogue System. Previous methods for CMC limit the vision on CMC task, neglecting that Recognizing Textual Entailment(RTE) task has much similarities with CMC task. In this paper, we propose a transfer learning model, which can take advantage of commonsense knowledge in RTE task by mapping CMC examples and RTE examples to a shared feature space and comprehending in this feature space. Specifically, we first establish a transfer learning framework which has three components: (1) source and target mappings, (2) domain regularization, and (3) CMC score function. Then we make selection for each component in our transfer learning framework and propose the Domain Transfer Comprehension(DTC) model. Experiments on Story Cloze Test show that our model outperforms most previous approaches and provides competitive results with state-of-art methods. We also show each components of our model have positive effect on performance.
Using a Disjoint Skill Model for Game and Task Difficulty in Human Computation Games

Prior research has used player rating systems to balance difficulty in human computation games (HCGs) without having to modify their levels by assigning ratings to levels to indicate level difficulty. Skill chains have also been used to define difficulty progressions for such games. Both these methods typically involve associating a level with a single rating or set of skills as being representative of the difficulty of both the in-game mechanics of the level and the complexity of the task that it models, taken together as a single unit. Though effective, this may not be suitable for HCGs where the game and the task being modeled require different sets of skills and abilities. To this end, we introduce a disjoint skill model that separately tracks game and task skill and difficulty in a 2D platformer HCG. We find that the disjoint model enables players to solve more difficult tasks compared to a baseline model.
Generating 1 Minute Summaries of Day Long Egocentric Videos

The popularity of egocentric cameras and their always-on nature has lead to the abundance of day-long first-person videos. Because of the extreme shake and highly redundant nature, these videos are difficult to watch from beginning to end and often require summarization tools for their efficient consumption. However, traditional summarization techniques developed for static surveillance videos, or highly curated sports videos and movies are, either, not suitable or simply do not scale for such hours long videos in the wild. On the other hand, specialized summarization techniques developed for egocentric videos limit their focus to important objects and people. In this paper, we present a novel unsupervised reinforcement learning technique to generate video summaries from day long egocentric videos. Our approach can be adapted to generate summaries of various lengths making it possible to view even 1-minute summaries of one's entire day. The technique can also be adapted to various rewards, such as distinctiveness and indicativeness of the summary. When using the facial saliency-based reward, we show that our approach generates summaries focusing on social interactions, similar to the current state-of-the-art (SOTA). Quantitative comparison on the benchmark Disney dataset shows that our method achieves significant improvement in Relaxed F-Score (RFS) (32.56 vs. 19.21) and BLEU score (12.12 vs. 10.64). Finally, we show that our technique can be applied for summarizing traditional, short, hand-held videos as well, where we improve the SOTA F-score on benchmark SumMe and TVSum datasets from 41.4 to 45.6 and 57.6 to 59.1 respectively.
Mobile apps use in indigenous languaje education of pre school children of Huitoto people in Peruvian Amazon

It includes results of a mobile app development and validation process made for children of pre school education in the indigenous community Centro Arenal (Huitoto ethnic group), to learn the basic content of their parents' native language through mobile devices (cell phones and tablets). And once that strengthen their cultural identity. Since 10 years ago Loreto is the last in educational indicators. In the Peruvian Amazon, 43 official languages are spoken, 27 of them in Loreto (all the Amazonian languages are in danger of disappearing). The intercultural bilingual education system is very limited (single-parent, multi-grade and multi-course) also, there is not educational content printed in indigenous languages. The results were favorable, being the application a relevant alternative, usable and accessible for educational institutions and Huitoto families.
Does Creating Shared Projects in Virtual Reality Capture Students’ Interest in Technology? An International Project in STEM Education

Virtual reality (VR) provides an immersive setting where it is possible to experience the perception of being present in a different place or environment. The TalkTech international project matches students at universities in the United States and Romania, to work as members of international teams as a virtual mobility. They research applications of VR, visit locations in their home countries related to their team's assigned cultural landmark, and create VR scenes to share with international partners. Team members demonstrate, share, compare, and discuss their work using online communication tools of their choice. This project-based learning provides new advances in STEM education by using digital media, as students experience how technology enables them to communicate and work in a collaborative global environment, create and share digital content, and gain insights into the culture of their partners' countries. Informal results suggest that the project sparks students' interest in possible future business applications of VR.
Differentiable Kernel Evolution

This paper proposes a differentiable kernel evolution (DKE) algorithm to find a better layer-operator for the convolutional neural network. Unlike most of the other neural architecture searching (NAS) technologies, we consider the searching space in a fundamental scope: kernel space, which encodes the assembly of basic multiplyaccumulate (MAC) operations into a conv-kernel. We first deduce a strict form of the generalized convolutional operator by some necessary constraints and construct a continuous searching space for its extra freedom-of-degree, namely, the connection of each MAC. Then a novel unsupervised greedy evolution algorithm called gradient agreement guided searching (GAGS) is proposed to learn the optimal location for each MAC in the spatially continuous searching space. We leverage DKE on multiple kinds of tasks such as object classification, face/object detection, large-scale finegrained and recognition, with various kinds of backbone architecture. Not to mention the consistent performance gain, we found the proposed DKE can further act as an autodilated operator, which makes it easy to boost the performance of miniaturized neural networks in multiple tasks.
You only Lie Twice: A Multi-round Cyber Deception Game of Questionable Veracity

Cyber deception focuses on providing advantage to defenders through manipulation of the information provided to attackers. Game theory is one of the methods that has been used to model cyber deception. In this work, we first introduce a simple game theoretic model of deception that captures the essence of interactions between an attacker and defender in a cyber environment. Second, we derive closed form expressions for a version of this model that capture the optimal strategies for both the attacker and defender. Third, we identify the potential behavior of an attacker at noncritical points via a Markov Decision Process (MDP) simulation.
Fuzzy Set Theory and Fuzzy Logic for Activities Automation in Engineering Education

Fuzzy set theory and Fuzzy logic propose bases for modeling, evaluation and optimization of tasks, events and processes in different areas. Recently, it has been utilized for the purpose of engineering education, giving possibilities for description of uncertain and complex systems leading to clear and precise results. This paper summarizes and analyzes applications of Fuzzy set theory and Fuzzy logic for improvement and automation of teaching and learning tasks. A model for utilization of Fuzzy apparatus in engineering educational practice is developed to facilitate educators and researchers in their everyday and unforeseen activities.
New geological, geochronological and geochemical characterization of the San Dimas mineral system: Evidence for a telescoped Eocene-Oligocene Ag/Au deposit in the Sierra Madre Occidental, Mexico

The San Dimas district is a historical world class Ag/Au epithermal deposit located in the Sierra Madre Occidental (SMO) of western Mexico. San Dimas has been classified as a classic low to intermediate sulfidation Ag/Au epithermal deposit (quartz + adularia + sericite type) developed during a single hydrothermal pulse of ~10 Ma, associated to the emplacement of intermediate intrusive bodies of Eocene K-Ar ages. However, this metallogenetic model includes several ambiguities, such as K-Ar cooling ages incompatible with the local and regional magmatic pulses, as well as wide differences in Ag-Au ratios between individual veins, which were also emplaced in two different structural systems. Based on a detailed study of mineralized veins, including new petrographic observations, new geochronological data (zircon U-Pb, adularia 40Ar/39Ar, and titanite fission track ages, FT), zircon trace-element composition, and geochemistry of gold and silver minerals, we demonstrate that San Dimas exhibits multiple mineralization events developed during different magmatic and tectonic episodes from Late Cretaceous to early Oligocene. The earliest episode is represented by Late Cretaceous copper – gold porphyry mineralization associated with fertile and oxidized magmas during the development of the Laramide arc. The second, most abundant ore mineralization is represented by a Ag-dominant vein system (adularia + rhodochrosite type), which developed into east-west striking fractures. New 40Ar/39Ar ages on adularia as well as new titanite FT ages for this mineralization event, together with one published 40Ar/39Ar age indicate the formation of Ag rich veins at ~41–40 Ma, shortly after the final emplacement of the Piaxtla batholith (~45 Ma), which formed from multiple fertile and oxidized magma intrusions in an extensional environment. The third mineralization episode is represented by Au-dominant epithermal veins (quartz sericite type), which were emplaced at ~31 Ma into north-south to NNE-SSE striking fractures. At regional scale these fractures also host rhyolitic domes associated with the first silicic ignimbrite flare up of the SMO in an extensional setting. The formation of the San Dimas district with its exceptionally rich Ag-Au mineralization is interpreted to be related to an extension-related late Eocene-early Oligocene regional uplift that allowed the overprinting of originally deeper Ag veins by a shallower Au mineralizing event.
The positive and negative effects of social media in India

research-article Share on The positive and negative effects of social media in India Authors: Niloy Ganguly IIT Kharagpur, India IIT Kharagpur, IndiaView Profile , Ponnurangam Kumaraguru IIIT Delhi, India IIIT Delhi, IndiaView Profile Authors Info & Claims Communications of the ACMVolume 62Issue 11November 2019 pp 98–99https://doi.org/10.1145/3345671Published:24 October 2019Publication History 1citation25,874DownloadsMetricsTotal Citations1Total Downloads25,874Last 12 Months2,068Last 6 weeks11 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Instance-based entropy fuzzy support vector machine for imbalanced data

Imbalanced classification has been a major challenge for machine learning because many standard classifiers mainly focus on balanced datasets and tend to have biased results toward the majority class. We modify entropy fuzzy support vector machine (EFSVM) and introduce instance-based entropy fuzzy support vector machine (IEFSVM). Both EFSVM and IEFSVM use the entropy information of k-nearest neighbors to determine the fuzzy membership value for each sample which prioritizes the importance of each sample. IEFSVM considers the diversity of entropy patterns for each sample when increasing the size of neighbors, k, while EFSVM uses single entropy information of the fixed size of neighbors for all samples. By varying k, we can reflect the component change of sample’s neighbors from near to far distance in the determination of fuzzy value membership. Numerical experiments on 35 public and 12 real-world imbalanced datasets are performed to validate IEFSVM, and area under the receiver operating characteristic curve (AUC) is used to compare its performance with other SVMs and machine learning methods. IEFSVM shows a much higher AUC value for datasets with high imbalance ratio, implying that IEFSVM is effective in dealing with the class imbalance problem.
Edge-Assisted Image Processing with Joint Optimization of Responding and Placement Strategy

Cloud computing is a prevailing approach for running various data-analysis algorithms to obtain insight from massive raw data. However, the current cloud computing architecture suffers from large computation and communication overhead when processing data in big size. In this work, we develop a systematic approach to efficiently process large-scale image processing tasks under an edge-assisted cloud computing architecture. We use a two-stage processing mechanism, which is to pre-process raw data on edge servers and feed only the most relevant information to the cloud, for the purpose of reducing system overhead. We model and formulate a problem that jointly optimizes the responding strategy and task placement strategy. We then propose a solution with a queuing mechanism to capture the impact of background workload on heterogeneous edge devices. We implement our system and use extensive experiments to validate its efficacy and efficiency in practice.
Learning Adaptive Regularization for Image Labeling Using Geometric Assignment

Abstract We study the inverse problem of model parameter learning for pixelwise image labeling, using the linear assignment flow and training data with ground truth. This is accomplished by a Riemannian gradient flow on the manifold of parameters that determines the regularization properties of the assignment flow. Using the symplectic partitioned Runge–Kutta method for numerical integration, it is shown that deriving the sensitivity conditions of the parameter learning problem and its discretization commute. A convenient property of our approach is that learning is based on exact inference. Carefully designed experiments demonstrate the performance of our approach, the expressiveness of the mathematical model as well as its limitations, from the viewpoint of statistical learning and optimal control.
Automated Scoring for Essay Questions In E-learning

Abstract E-learning is employing technology to help and promote learning begun decades ago.one of most important example of using intelligent software agent in e-learning is Intelligent Tutoring systems (ITSs). Assessment plays a significant role in the educational process. Automated Essay Scoring (AES) is defined as the computer technology that evaluates and scores the subjective answers. To evaluate students essay answers based on a linguistic knowledge in this project we presented a suitable model. Getting results from the recommended system application in calculating simulation results indicates high precision in performance associated with other methods. We used square root error rate (RMSE) and Pearson correlation coefficient metrics to measure the system efficiency and also use student answer acquired from the University of North Texas data collection, this data accessible on the Internet.
Is it a New Tulip Mania Age?

Although cryptocurrencies and blockchain technology can be considered new advances, they have started to be recognized widely, and this has been discussed and investigated in lots of research studies. In parallel, the primary purpose of this study is to investigate the development and evolution of cryptocurrencies and blockchain technology over the past years in the academic world. To this end, 334 scholarly journal articles are examined to: (1) conduct a comprehensive literature review in the field of cryptocurrencies and blockchain; (2) identify the possible trends and changes in this field over the ten years; (3) compare the publishing productivity of journals; and (4) guide future research in this field. The results highlight that the researchers mainly concentrate on legal and ethical issues of them; their benefits, challenges, and risks; their conceptualization, evolution, and future; the economic dimension of them; and financial and accounting related issues of them.
Ensemble of fine‐tuned convolutional neural networks for urine sediment microscopic image classification

In this study, an ensemble of fine‐tuned convolutional neural networks (CNNs) is proposed. As CNN training requires large annotated data, which are lacking in the field of urine sediment microscopic image processing, the authors first pre‐trained the CNNs, including ResNet50 and GoogLeNet, and developed AlexNet on an ImageNet dataset. Thereafter, some of the weights of the pre‐trained CNNs were transferred to the urine sediment microscopic image dataset. To guide fine‐tuning of the learning rate and cascading features, the hierarchical nature of features in different convolutional layers was investigated by visualising the CNN. Then, they combined three CNNs as an ensemble of CNNs to decrease the differences and impurity interference among features of urine sediment microscopic image. These fusion features were employed to train the fully connected neural network for classification. In this study, they improved the accuracy of each CNN by an average of 2.2% through fine‐tuning of the learning rate and cascading features. Moreover, the better experimental results were achieved compared with other state‐of‐the‐art methods and indicated that a 97% classification accuracy can be attained.
Real-Time Object Detection On Low Power Embedded Platforms

Low power real-time object detection is an interesting application in deep learning with applications in smart wearables, Advanced Driver Assistance Systems (ADAS), drone surveillance systems, etc. In this paper, we discuss the limitations with existing networks and enumerate the various factors to keep in mind while designing neural networks for a target hardware. Based on our experience of working with TI embedded platform, we provide a systematic approach for designing real time object detection networks on low power embedded platforms. First stage involves identifying the optimal layers for the hardware, by understanding it's computational and memory limitations. The next step is to use these layers to come up with a basic building block that has low computational complexity. The final stage involves using model compression techniques like sparsification/quantization to accelerate the inference process. Based on this design approach, we were able to come up with a low latency object detection model HX-LPNet that operates at 22 FPS on low power TDA2PX System on Chip(SoC) provided by Texas Instruments (TI).
Alpha divergence minimization in multi-class Gaussian process classification

This paper analyzes the minimization of α-divergences in the context of multi-class Gaussian process classification. For this task, several methods are explored, including memory and computationally efficient variants of the Power Expectation Propagation algorithm, which allow for efficient training using stochastic gradients and mini-batches. When these methods are used for training, very large datasets (several millions of instances) can be considered. The proposed methods are also very general as they can interpolate between other popular approaches for approximate inference based on Expectation Propagation (EP) (α → 1) and Variational Bayes (VB) (α → 0) simply by varying the α parameter. An exhaustive empirical evaluation analyzes the generalization properties of each of the proposed methods for different values of the α parameter. The results obtained show that one can do better than EP and VB by considering intermediate values of α.