Reviewerly/exHarmony huggingface dataset:
A Generalization Bound for Online Variational Inference.

Bayesian inference provides an attractive online-learning framework to analyze sequential data, and offers generalization guarantees which hold even with model mismatch and adversaries. Unfortunately, exact Bayesian inference is rarely feasible in practice and approximation methods are usually employed, but do such methods preserve the generalization properties of Bayesian inference ? In this paper, we show that this is indeed the case for some variational inference (VI) algorithms. We consider a few existing online, tempered VI algorithms, as well as a new algorithm, and derive their generalization bounds. Our theoretical result relies on the convexity of the variational objective, but we argue that the result should hold more generally and present empirical evidence in support of this. Our work in this paper presents theoretical justifications in favor of online algorithms relying on approximate Bayesian methods.
Enhancing the operational value of snowpack models with visualization design principles

Abstract. Forecasting snow avalanches requires a reliable stream of field observations, which are often difficult and expensive to collect. Despite the increasing capability of simulating snowpack conditions with physical models, models have seen limited adoption by avalanche forecasters. Feedback from forecasters suggests that model data are presented in ways that are difficult to interpret and irrelevant to operational needs. We apply a visualization design framework to enhance the value of snowpack models to avalanche forecasters. An established risk-based avalanche forecasting workflow is used to define the ways forecasters solve problems with snowpack data. We suggest that model data be visualized in ways that directly support common forecasting tasks such as identifying snowpack features related to avalanche problems and locating avalanche problems in terrain at relevant spatial scales. Examples of visualizations that support these tasks and follow established perceptual and cognitive principles from the field of information visualization are presented. Interactive designs play a critical role in understanding these complex datasets and are well suited for forecasting workflows. Although extensive user testing is still needed to evaluate the effectiveness of these designs, visualization design principles open the door to more relevant and interpretable applications of snowpack model for avalanche forecasters. This work sets the stage for implementing snowpack models into visualization tools where forecasters can test their operational value and learn their capabilities and deficiencies.
Scheduler of quantum circuits based on dynamical pattern improvement and its application to hardware design

As quantum hardware increases in complexity, successful algorithmic execution relies more heavily on awareness of existing device constraints. In this work we focus on the problem of routing quantum information across the machine to overcome the limited connectivity of quantum hardware. Previous approaches address the problem for each two-qubit gate separately and then impose the compatibility of the different routes. Here we shift the focus onto the set of all routing operations that are possible at any given time and favor those that most benefit the global pattern of two-qubit gates. We benchmark our optimization technique by scheduling variational algorithms for transmon chips. Finally we apply our scheduler to the design problem of quantifying the impact of manufacturing decisions. Specifically, we address the number of distinct qubit frequencies in superconducting architectures and how they affect the algorithmic performance of the quantum Fourier transform.
How to Reduce Anomaly Detection in Images to Anomaly Detection in Noise

Anomaly detectors address the difficult problem of detecting automatically exceptions in a background image, that can be as diverse as a fabric or a mammography.Detection methods have been proposed by the thousands because each problem requires a different background model.By analyzing the existing approaches, we show that the problem can be reduced to detecting anomalies in residual images (extracted from the target image) in which noise and anomalies prevail.Hence, the general and impossible background modeling problem is replaced by a simple noise model, and allows the calculation of rigorous detection thresholds.Our approach is therefore unsupervised and works on arbitrary images.The residual images can favorably be computed on dense features of neural networks.Our detector is powered by the a contrario detection theory, which avoids over-detection by fixing detection thresholds taking into account the multiple tests.
Reducing tail latency using duplication

Duplication can be a powerful strategy for overcoming stragglers in cloud services, but is often used conservatively because of the risk of overloading the system. We call for making duplication a first-class concept in cloud systems, and make two contributions in this regard. First, we present duplicate-aware scheduling or DAS, an aggressive duplication policy that duplicates every job, but keeps the system safe by providing suitable support (prioritization and purging) at multiple layers of the cloud system. Second, we present the D-Stage abstraction, which supports DAS and other duplication policies across diverse layers of a cloud system (e.g., network, storage, etc.). The D-Stage abstraction decouples the duplication policy from the mechanism, and facilitates working with legacy layers of a system. Using this abstraction, we evaluate the benefits of DAS for two data parallel applications (HDFS, an in-memory workload generator) and a network function (Snort-based IDS cluster). Our experiments on the public cloud and Emulab show that DAS is safe to use, and the tail latency improvement holds across a wide range of workloads.
Educational Website Ranking Using Fuzzy Logic and K-Means Clustering Based Hybrid Method

In the Internet era, it is a huge challenge for users to find suitable and pertinent information out of the huge amount of online data.The challenge is particularly arduous for students searching for education information in a specific domain.To solve the problem, this paper puts forward an educational website ranking method, which applies fuzzy logic and k-means clustering in sequence.First, a fuzzy inference system (FIS) was established based on the fuzzy logic, and used to find the utility value (UV) of an educational website according to the feedback marks of each student.Then, the general utility value (GUV) of each educational website was determined through k-means clustering of all the UVs of that website.Then, the educational websites were ranked by their GUVs.The experimental results show that the proposed method ranks the educational websites clearly and correctly, enabling students to find the desired education information.
SemEval-2016 Task 4: Sentiment Analysis in Twitter

This paper discusses the fourth year of the ``Sentiment Analysis in Twitter Task''. SemEval-2016 Task 4 comprises five subtasks, three of which represent a significant departure from previous editions. The first two subtasks are reruns from prior years and ask to predict the overall sentiment, and the sentiment towards a topic in a tweet. The three new subtasks focus on two variants of the basic ``sentiment classification in Twitter'' task. The first variant adopts a five-point scale, which confers an ordinal character to the classification task. The second variant focuses on the correct estimation of the prevalence of each class of interest, a task which has been called quantification in the supervised learning literature. The task continues to be very popular, attracting a total of 43 teams.
FPGA-Based Annealing Processor with Time-Division Multiplexing

An annealing processor based on the Ising model is a remarkable candidate for combinatorial optimization problems and it is superior to general von Neumann computers. CMOS-based implementations of the annealing processor are efficient and feasible based on current semiconductor technology. However, critical problems with annealing processors remain. There are few simulated spins and inflexibility in terms of implementable graph topology due to hardware constraints. A prior approach to overcoming these problems is to emulate a complicated graph on a simple and high-density spin array with so-called minor embedding, a spin duplication method based on graph theory. When a complicated graph is embedded on such hardware, numerous spins are consumed to represent high-degree spins by combining multiple low-degree spins. In addition to the number of spins, the quality of solutions decreases as a result of dummy strong connections between the duplicated spins. Thus, the approach cannot handle large-scale practical problems. This paper proposes a flexible and scalable hardware architecture with time-division multiplexing for massive spins and high-degree topologies. A target graph is separated and mapped onto multiple virtual planes, and each plane is subject to interleaved simulation with time-division processing. Therefore, the behavior of high-degree spins is efficiently emulated over time, so that no dummy strong connections are required, and the solution quality is accordingly improved. We implemented a prototype hardware design for FPGAs, and we evaluated the proposed method in a software-based annealing processor simulator. The results indicate that the method increased the spins that can be deployed. In addition, our time-division multiplexing architecture improved the solution quality and convergence time with reasonable resource consumption.
Enhancing Intercultural Competence with 360-Degree Virtual Reality Videos

ABSTRACT For the past few decades, computer technology has been increasingly used in all aspects of language learning. Virtual reality (VR), which allows learners to experience immersive environments through the use of VR goggles, has been found to change their perceptions, behaviors, attitudes, and empathy levels, all of which are critical for enhancing intercultural competence. Although there is a growing body of research on the pedagogical advantages of VR applications, hardly any studies have examined how it might enhance intercultural competence in the foreign-language classroom. In part, this absence may be due to a lack of appropriate VR-based foreign-language curricula informed by theory and sound instructional principles. In light of this, this study proposes a curriculum initiative that incorporates 360-degree VR videos for cultural learning in college-level foreign-language classes. After introducing the potential benefits of VR technologies, it highlights relevant learning theories and provides specific classroom examples of VR for intercultural competence, before concluding with implications and recommendations for future curriculum design.
A Robust and Blind Watermarking for Color Videos Using Redundant Wavelet Domain and SVD

This paper presents a color video watermarking scheme using RedundantPrasanth Vaidya, S. Discrete Wavelet Transform (RDWT) and Singular Value DecompositionChandra Mouli, P. V. S. S. R. (SVD) with good imperceptibility and high robustness. The binary watermark is embedded into blocks of low-level sub-band of the wavelet transform. Using blind extraction process, the watermark is extracted from the watermarked color video. The proposed watermarking scheme protects the copyright information from adversaries and also helps in claiming the ownership of the video content. From the experimental results, it is found that the proposed scheme is robust to all possible attacks and has better results when compared to existing watermarking schemes.
Work Ethics and Social Responsibility: Actual and Aspiration

This study aims at investigating the reality of social responsibility, and its ethical dimensions in educational business organizations in addition to knowing the prevailing advantages and disadvantages. The sample of the study consisted of northern Jordanian universities were 210 male and female as respondents to a questionnaire. 200 questionnaires were valid for statistical analysis in order to achieve the purposes of this study; the researcher adopted a descriptive approach. This study utilized a tool to measure the social responsibility, and its ethical dimensions in private northern Jordanian universities. The study concluded that the correlation between social responsibility and ethical dimensions were statistically significant. In light of the aforementioned findings, the study recommended the raising employees’ morals and motivating them in ethical ways, in addition to developing a clear plan applied by educational organizations to apply and practice social responsibility.
Experimental simulation of hybrid quantum systems and entanglement on a quantum computer

We propose the utilization of the IBM Quantum Experience quantum computing system to simulate different scenarios involving common hybrid quantum system components, the nitrogen vacancy center (NV center), and the flux qubit. We perform a series of simulation experiments and demonstrate properties of a virtual hybrid system, including its spin relaxation rate and state coherence. In correspondence with experimental investigations, we look at the scalability of such systems and show that increasing the number of coupled NV centers decreases the coherence time. We also establish the main error rate as a function of the number of control pulses in evaluating the fidelity of the four qubit virtual circuit with the simulator. Our results show that the virtual system can attain decoherence and fidelity values comparable to what has been reported for experimental investigations of similar physical hybrid systems, observing a coherence time at 0.35 s for a single NV center qubit and fidelity in the range of 0.82. This work thus establishes an effective simulation protocol to test and evaluate different technologies as a supplementary measure that complements their experimental operation.
Congruence from the operator’s point of view

Abstract A basic sanity property of a process semantics is that it constitutes a congruence with respect to standard process operators. This issue has been traditionally addressed by developing, for a specific process semantics, a syntactic format for operational semantics specifications. We suggest a novel, orthogonal approach, which focuses on a specific process operator and determines a class of congruence relations for this operator. To this end, we impose syntactic restrictions on Hennessy–Milner logic, so that a process semantics whose modal characterization satisfies those criteria is guaranteed to be a congruence with respect to the operator in question. We investigate alternative composition, action prefix, projection, encapsulation, renaming, and parallel composition with communication, in the context of both concrete and weak process semantics.
End-to-End Hand Kinematic Decoding from LFPs Using Temporal Convolutional Network

In recent years, local field potentials (LFPs) have emerged as a promising alternative input signal for brain-machine interfaces (BMIs). Several studies have demonstrated that LFP-based BMIs could provide long-term recording stability and, at the same time, comparable decoding performance to their spike counterparts. However, despite the compelling results, most LFP-based BMIs still make use of hand-crafted features which is a time-consuming process and can be suboptimal. In this paper, we propose an end-to-end system approach based on temporal convolutional network (TCN) to automatically extract features and decode kinematics of hand movements directly from raw LFP signals. We benchmark its decoding performance against traditional approaches incorporating long short-term memory (LSTM) decoders driven by hand-crafted LFP features. Experimental results demonstrate significant performance improvement of the proposed approach compared to the traditional approaches, demonstrating the suitability and the potential of TCN-based end-to-end systems in providing stable and high decoding performance LFP-based BMIs.
Fostering Students' Self-Esteem in Speaking by Extending Speaking Activities in Social Media

Speaking in foreign language involves both cognitive and psychological process which eventually bring it as the most challenging skill to learn. However, many learners do not have adequate opportunities to practice speaking unless in the classroom, thus, teachers and students needed to developed various extensive speaking activities including utilizing technological features such as social media. This study is aimed at describing the students' perceptions about extending speaking activities in social media and how it impacts on the improvement of their skill as well as their self-confidence in speaking. It was a case study involving 5 students of English Department in Universitas Katolik Indonesia Santu Paulus Ruteng, East Nusa Tenggara as the participants. The data were collected by observing the participants’ English-Speaking videos posted on Facebook and Focus Group Discussion. The observation sheet and the discussion guidelines were used as the instruments of data collection. To analyze the data, the researchers used Miles and Huberman’s procedures of data analysis, comprising: data reduction, data displaying, and conclusion drawing. This study revealed that extending the speaking activities in social media had positively enhanced the students’ speaking ability as well as their self-esteem in speaking.
Some (social) aspects of education in transition Serbia

The paper examines the changes that education in Serbia - especially high education - has undergone during the transition period, 1989-2019. Based on the performed analysis, the author proposes to periodize changes in education and educational policy in transition Serbia. During this three-decade-long transition period, there is a clear distinction between pre-Bologna and Bologna phases in higher education. It is pointed out that the ideological basis of transitional education reform in Serbia, and especially its "Bologna phase", is NEO-liberal, globalist and anti-Enlightenment. The purpose of education has changed, so that educational institutions have become "enterprises", which, instead of having a role of spreading enlightenment and shaping personalities with broad education, have the function of making a profit on education and, at the same time, educating young people for occupations that should contribute to GDP growth as the main criterion for the merchantability of workforce qualifications. It is clear that the results of the whole process were: the inflation of the highly educated, emigration of the best experts, the retainment of mediocre and weak experts, the lack of workers of all kinds, the promotion of technical "knowledge", various forms of "systemic educational corruption" and neglect of the social sciences and humanities. This, of course, should also be observed through the prism of "global division of labor": peripheral or semi-peripheral countries "export" their best experts to the center countries, like they do with other raw materials, thus financing the prosperity of the rich, while at the same time remaining in stagnant or even regressive position.
Vehicle Tracking Using Video Surveillance

In numerous applications including the security of individual vehicles as well as public transportation frameworks, the ability to follow or track vehicles is very helpful.Using computer vision and deep learning algorithms, the project deals with the concept of vehicle tracking in real-time based on continuous video stream from a CCTV camera to track the vehicles.The tracking system is tracking by detection paradigm.YOLOv3 object detection is applied to achieve faster object detection for real-time tracking.By implementing and improving the ideas of Deep SORT tracking for better occlusion handling, a better tracking system suitable for real-time vehicle tracking is presented.So as to demonstrate the achievability and adequacy of the framework, this chapter presents exploratory consequences of the vehicle following framework and a few encounters on handy executions.
Getting quantifying-into questions uniformly: Functionality, domain exhaustivity, and quantificational variability

Questions with a quantifier have readings that seem to involve quantification-into questions (QIQ). This paper provides a uniform compositional analysis of these readings. I propose that QIQ-readings are special functional readings —— the question nucleus involves a functional dependency between the quantifier and wh-trace.I argue that the seeming QIQ-effect is derived by extracting a minimal set that satisfies a quantificational predication condition. The possible values of this minimal set determine whether QIQ-readings are available and whether a question admits a pair-list answer and/or a choice answer. This analysis also explains the contrast between ∀-questions and multi-wh questions with respect to domain exhaustivity, and accounts for the quantificational variability effects in embeddings.
FasterSeg: Searching for Faster Real-time Semantic Segmentation

We present FasterSeg, an automatically designed semantic segmentation network with not only state-of-the-art performance but also faster speed than current methods. Utilizing neural architecture search (NAS), FasterSeg is discovered from a novel and broader search space integrating multi-resolution branches, that has been recently found to be vital in manually designed segmentation models. To better calibrate the balance between the goals of high accuracy and low latency, we propose a decoupled and fine-grained latency regularization, that effectively overcomes our observed phenomenons that the searched networks are prone to "collapsing" to low-latency yet poor-accuracy models. Moreover, we seamlessly extend FasterSeg to a new collaborative search (co-searching) framework, simultaneously searching for a teacher and a student network in the same single run. The teacher-student distillation further boosts the student model's accuracy. Experiments on popular segmentation benchmarks demonstrate the competency of FasterSeg. For example, FasterSeg can run over 30% faster than the closest manually designed competitor on Cityscapes, while maintaining comparable accuracy.
Functional vs. parametric equivalence of ReLU networks

We address the following question: How redundant is the parameterisation of ReLU networks? Specifically, we consider transformations of the weight space which leave the function implemented by the network intact. Two such transformations are known for feed-forward architectures: permutation of neurons within a layer, and positive scaling of all incoming weights of a neuron coupled with inverse scaling of its outgoing weights. In this work, we show for architectures with non-increasing widths that permutation and scaling are in fact the only function-preserving weight transformations. For any eligible architecture we give an explicit construction of a neural network such that any other network that implements the same function can be obtained from the original one by the application of permutations and rescaling. The proof relies on a geometric understanding of boundaries between linear regions of ReLU networks, and we hope the developed mathematical tools are of independent interest.