Reviewerly/exHarmony huggingface dataset:
Principles for the Design of an Educational Voice Assistant for Learning Java

Conversational agents, be they text- or voice-powered, are acquiring a level of maturity that makes them useful for natural and smart interactions. In this paper, we explore some design principles for voice-commanded assistants for educational use, in particular, of one designed to train Java concepts as a complement to a MOOC (Massive Open Online Course) about programming.
A Critique of Game-Based Definitions of Receipt-Freeness for Voting

We analyse three game-based definitions of receipt-freeness; uncovering soundness issues with two of the definitions and completeness issues with all three. Hence, two of the definitions are too weak, i.e., satisfiable by voting schemes that are not intuitively receipt-free. More precisely, those schemes need not even satisfy ballot secrecy. Consequently, the definitions are satisfiable by schemes that reveal how voters vote. Moreover, we find that each definition is limited in scope. Beyond soundness and completeness issues, we show that each definition captures a different attacker model and we examine some of those differences.
Making it More Secure

As High Performance Computing and Big Data analytics become more commonplace, we see researchers applying these tools in new areas. Indeed, in the past few years, we've seen the use of HPC in diverse areas such as archeology, public policy, and digital humanities. So it comes as no surprise that many life science researchers are now approaching us to use large scale computation and data analytics on their sensitive data sets, such as de-identified patient or genomics data, for the purposes of scientific inquiry. At UC Berkeley, this has become a pressing issue, as existing faculty need a place to do research on sensitive data. And we knew of at least one instance where it affected the campus' ability to recruit a new faculty member. We had a clear imperative for action!
Crafting Efficient Neural Graph of Large Entropy

Network pruning is widely applied to deep CNN models due to their heavy computation costs and achieves high performance by keeping important weights while removing the redundancy. Pruning redundant weights directly may hurt global information flow, which suggests that an efficient sparse network should take graph properties into account. Thus, instead of paying more attention to preserving important weight, we focus on the pruned architecture itself. We propose to use graph entropy as the measurement, which shows useful properties to craft high-quality neural graphs and enables us to propose efficient algorithm to construct them as the initial network architecture. Our algorithm can be easily implemented and deployed to different popular CNN models and achieve better trade-offs.
SYNTACTICAL ERROR ANALYSIS ON REPORT TEXT

This study aims at investigating and classifying the syntactical errors in the writing of report text made by 20 students of Department of English Literature of FIB-USU. Syntactical errors in writing made by EFL students are needed to be investigated by using the Error Analysis theory developed by Gass &amp; Selinker in 2008 as it provides six appropriate investigation procedures, namely; collecting data, identifying errors, classifying errors, quantifying errors, analyzing errors and remediation. Each of students was assigned to write a topic-based report text with the length of 150 up to 250 words in count. This study dealt with a qualitative descriptive approach. After the EA procedures applied, in this study found that the students made major syntactical errors within the use of article ‘a/an’,’ the’ in terms of omission and addition of articles, the use of relative pronoun, and the use of subject-verb agreement in terms of past tense agreement and number agreement. Based on the error analysis procedure applied, it was obtained that the most predominant syntactical errors made by the students was the use of article which comprises of 125 errors (50.2%), followed by the use of subject-verb agreement with 117 errors (47%) and followed by the use of relative pronoun as the least error which comprises of 7 errors (2.8%). The syntactical error made by the students caused by two major sources, intra-language error and intra-language error. By seeing at these problematic areas, the researcher has suggested to take out several related pedagogical remediation to the students.
The State Sovereignty in Questions of Issue of Cryptocurrency

The analysis of powers and order of monetary issue by the Central Bank of Russia in the system of the relations of the state sovereignty is carried out. Is considered contradictions of independent position of the Central Bank and the Government of the Russian Federation in questions of the state economic policy. Is defined that the fiat monetary system is based only on the authority of the Central bank and government whereas cryptocurrency – on the system of own safety and a way of its generation: blockchain. Is defined that in the independence and activity of the Central Bank of the Russian Federation can not consider “wishes” and plans of the government and Senate of Russia. Positive and negative properties of cryptocurrency in a modern financial system are considered. The cryptocurrency is considered out of communication with a monetary system, but only as subject to financial manipulations. It is necessary to change ideology of monetary issue. Non-cash money, special drawing right and so forth are considered as cryptocurrency prerequisites, but controlled by the currency center. Whereas the sense of cryptocurrency consists in its decentralization. The release of money has to be provided with a national product. The offer on establishment of legal bases of regulation of process of monetary issue is made.
Iterated Belief Base Revision: A Dynamic Epistemic Logic Approach

AGM’s belief revision is one of the main paradigms in the study of belief change operations. In this context, belief bases (prioritised bases) have been largely used to specify the agent’s belief state - whether representing the agent’s ‘explicit beliefs’ or as a computational model for her belief state. While the connection of iterated AGM-like operations and their encoding in dynamic epistemic logics have been studied before, few works considered how well-known postulates from iterated belief revision theory can be characterised by means of belief bases and their counterpart in dynamic epistemic logic. This work investigates how priority graphs, a syntactic representation of preference relations deeply connected to prioritised bases, can be used to characterise belief change operators, focusing on well-known postulates of Iterated Belief Change. We provide syntactic representations of belief change operators in a dynamic context, as well as new negative results regarding the possibility of representing an iterated belief revision operation using transformations on priority graphs.
Visualizing RNN States with Predictive Semantic Encodings

Recurrent Neural Networks are an effective and prevalent tool used to model sequential data such as natural language text. However, their deep nature and massive number of parameters pose a challenge for those intending to study precisely how they work. We present a visual technique that gives a high level intuition behind the semantics of the hidden states within Recurrent Neural Networks. This semantic encoding allows for hidden states to be compared throughout the model independent of their internal details. The proposed technique is displayed in a proof of concept visualization tool which is demonstrated to visualize the natural language processing task of language modelling.
Pembuatan Video Profil dengan Efek Vintage Kampung Wisata Adat Sengkoah sebagai Media Informasi

Central Lombok Regency is one of the districts that supports the establishment of Tourism Villages. One of the Tourism Villages that was recently initiated was the Sengkoah Traditional Tourism Village. This village has not been widely known by the wider community due to lack of information dissemination. Previously the dissemination of information was only through facebook pages and internet articles. In order to introduce this Sengkoah Indigenous Tourism Village, researchers created a profile video with vintage effects. Where this vintage effect will strengthen the impression of the past in the video part that is needed. In the process of making a profile video through three stages, namely, the first pre-production stage where the location survey and video drafting begin. The two stages of production are taking pictures and recording sound and preparing all the materials needed in the video. The three post-production stages are the video editing stage that brings together all the materials needed to become a complete video. After doing all of these steps, it is expected to produce a profile video of the Sengkoah Traditional Tourism Village with vintage effects. So that this video can be used as a more effective medium of information to introduce to the general public about the existence of this tourist village and the traditional activities offered. To find out the feasibility of the videos that have been made a beta test is proposed by submitting questionnaires to 30 respondents. The results obtained were profile videos with vintage effects of the traditional Sengkoah tourist village worthy of dissemination.
A 1D Compression Model for Loess Based on Disturbed State Concept

Despite being insufficiently compacted, the loess structure can withstand a huge load like the super solid.This paper attempts to disclose how water content affects the bonding and arrangement of loess particles, and how it disturbs the loess structure.For this purpose, five confined compression tests are designed for intact loess and remolded loess of different water contents.Specifically, the disturbance function was defined with void ratio as the parameter based on the disturbed state concept (DSC) theory, considering how the structural features of loess Q3 affects the compressive strength.Next, the evolution law of the disturbance function was explored, and the influence law of water content on the parameters of the disturbance function was investigated.Finally, a DSC-based 1D compression model was constructed for loess Q3.The research results show that the water content has a great impact on the evolution law of the 1D disturbance function and its two parameters, and the influence can be described by exponential function.Besides, the experimental results show that our model can accurately describe how water content affects loess compression.
Assessment IT Risk Management at The Computer and Network Laboratory School

The Computer and Network Engineering Laboratory School is a means for teaching and learning that has problems with information technology. In this research, IT Risk Management Assessment will be carried out at the Computer and Network Engineering Laboratory of School. This research was conducted to determine what risks of what happened. In this study, the NIST SP 800-30r1 Framework method will be used to obtain risk scores. While for data collection techniques will use qualitative analysis. The stages of risk assessment carried out include Prepare for Assessment, Conduct Assessment, Communicate Result, and Maintain Assessment. From the assessment stage, the level of risk obtained is in a moderate position. This assessment was taken from the results of interviews with five respondents in the organization management of The Computer and Network Engineering Laboratory School. Emerging risks have a serious impact on assets, organization and activities in The Computer and Network Engineering Laboratory School.
Data Integrity and Security using Keccak and Digital Signature Algorithm (DSA)

Data security is a very important compilation using cloud computing; one of the research that is running and using cloud technology as a means of storage is G-Connect. One of the developments made by the G-Connect project is about data security; most of the problems verification of the data sent. In previous studies, Keccak and RSA algorithms have implemented for data verification needs. But after a literature study of other algorithms that can make digital signatures, we found what is meant by an algorithm that is better than RSA in rectangular speeds, namely Digital Signature Algorithm (DSA).DSA is one of the key algorithms used for digital signatures, but because DSA still uses Secure Hash Algorithm (SHA-1) as an algorithm for hashes, DSA rarely used for data security purposes, so Keccak is used instead of the hash algorithm on DSA. Now, Keccak become the standard for the new SHA-3 hash function algorithm. Because of the above problems, the focus of this research is about data verification using Keccak and DSA. The results of the research are proven that Keccak can run on DSA work system, obtained a comparison of execution time process between DSA and RSA where both use Keccak.
Multiple Studies: The Influence of Collaborative Learning Approach on Indonesian Secondary High School Students’ English-Speaking Skills

To respond the globalization, the importance of English communication skills including speaking skills increases. Looking at Indonesian context, secondary high school students find spoken English is much more difficult than written English. Students are good at grammar, reading, and writing but they can hardly speak (Hairdara, 2016). This article proposes collaborative learning approach (CLA) as one suitable solution to solve the obstacles of sharpening English speaking skills due to its benefits. The discussion of this investigation highlights some benefits of CLA in developing speaking skills and its challenges need to be considered as well. Receiving well-proven evidence of its benefits, CLA is recommended to be implemented in teaching and learning English speaking skills. As results of this investigation, there are three main benefits of CLA that its implementation can reduce language anxiety, raise students’ participation, and increase students’ self-confidence.
An accelerated communication-efficient primal-dual optimization framework for structured machine learning

Distributed optimization algorithms are essential for training machine learning models on very large-scale datasets. However, they often suffer from communication bottlenecks. Confronting this issue, a communication-efficient primal-dual coordinate ascent framework (CoCoA) and its improved variant CoCoA+ have been proposed, achieving a convergence rate of O(1/t) for solving empirical risk minimization problems with Lipschitz continuous losses. In this paper, an accelerated variant of CoCoA+ is proposed and shown to possess a convergence rate of O(1/t2) in terms of reducing suboptimality. The analysis of this rate is also notable in that the convergence rate bounds involve constants that, except in extreme cases, are significantly reduced compared to those previously provided for CoCoA+. The results of numerical experiments are provided to show that acceleration can lead to significant performance gains.
The Oblivious Machine

We present an oblivious machine, a concrete notion for a multiparty random access machine (RAM) computation and a toolchain to allow the efficient execution of general programs written in a subset of C that allows RAM-model computation over the integers. The machine only leaks the list of possible instructions and the running time. Our work is based on the oblivious array for secret-sharing-based multiparty computation by Keller and Scholl (Asiacrypt ’14). This means that we only incur a polylogarithmic overhead over the execution on a CPU. We describe an implementation of our construction using the Clang compiler from the LLVM project and the SPDZ protocol by Damgård et al. (Crypto ’12). The latter provides active security against a dishonest majority and works in the preprocessing model. The online phase clock rate of the resulting machine is 41 Hz for a memory size of 1024 64-bit integers and 2.2 Hz for a memory of $$2^{20}$$ integers. Both timings have been taken for two parties in a local network. Similar work by other authors has only been in the semi-honest setting. To further showcase our toolchain, we implemented and benchmarked private regular expression matching. Matching a string of length 1024 against a regular expression with 69270 transitions as a finite state machine takes seven hours online time, of which more than six hours are devoted to loading the reusable program.
A Framework for Data-Driven Augmented Reality

This paper presents a new framework to support the creation of augmented reality (AR) applications for educational purposes in physics or engineering lab courses. These applications aim to help students to develop a better understanding of the underlying physics of observed phenomena. For each desired experiment, an AR application is automatically generated from an approximate 3D model of the experimental setup and precomputed simulation data. The applications allow for a visual augmentation of the experiment, where the involved physical quantities like vector fields, particle beams or density fields can be visually overlaid on the real-world setup. Additionally, a parameter feedback module can be used to update the visualization of the physical quantities according to actual experimental parameters in real-time. The proposed framework was evaluated on three different experiments: a Teltron tube with Helmholtz coils, an electron-beam-deflection tube and a parallel plate capacitor.
Content-oriented image quality assessment with multi-label SVM classifier

Image content is a fundamental attribute of images and plays an important role in human perception of image information. However, the influence of image content type, which is derived based on the classification of the image content, has been largely ignored in the image quality assessment (IQA). In this paper, a new IQA database based on the classification of image content is built. In particular, the database contains four content types, including landscape, human face, handcrafted scene and the hybrid scene. In total, 80 reference images with 20 images for each type of content are involved, and 1600 distorted images with mean opinion scores (MOSs) are generated by using five types and four levels of distortion. Furthermore, to classify these images, especially for the hybrid case, a Support Vector Machine (SVM) based multi-label (ML) classification is presented. Extensive experiments based on existing no reference IQA (NR-IQA) models show that content classification can greatly facilitate the image quality evaluation. The database and code are made publicly available at: https://github.com/jingchao17/Content-oriented-Database.
Exploiting the Sign of the Advantage Function to Learn Deterministic Policies in Continuous Domains

In the context of learning deterministic policies in continuous domains, we revisit an approach, which was first proposed in Continuous Actor Critic Learning Automaton (CACLA) and later extended in Neural Fitted Actor Critic (NFAC). This approach is based on a policy update different from that of deterministic policy gradient (DPG). Previous work has observed its excellent performance empirically, but a theoretical justification is lacking. To fill this gap, we provide a theoretical explanation to motivate this unorthodox policy update by relating it to another update and making explicit the objective function of the latter. We furthermore discuss in depth the properties of these updates to get a deeper understanding of the overall approach. In addition, we extend it and propose a new trust region algorithm, Penalized NFAC (PeNFAC). Finally, we experimentally demonstrate in several classic control problems that it surpasses the state-of-the-art algorithms to learn deterministic policies.
Deep Learning based Channel Estimation for Massive MIMO with Mixed-Resolution ADCs

In this article, deep learning is applied to estimate the uplink channels for mixed analog-to-digital converters (ADCs) massive multiple-input multiple-output (MIMO) systems, where a portion of antennas are equipped with high-resolution ADCs while others employ low-resolution ones at the base station. A direct-input deep neural network (DI-DNN) is first proposed to estimate channels by using the received signals of all antennas. To eliminate the adverse impact of the coarsely quantized signals, a selective-input prediction DNN (SIP-DNN) is developed, where only the signals received by the high-resolution ADC antennas are exploited to predict the channels of other antennas as well as to estimate their own channels. Numerical results show the superiority of the proposed DNN based approaches over the existing methods, especially with mixed one-bit ADCs, and the effectiveness of the proposed approaches on different ADC resolution patterns.
Chinese Rubbing Image Binarization based on Deep Learning for Image Denoising

Aiming at the problem of the chinese rubbing image segmentation under a denoising algorithm based on deep convolutional neural network is proposed. Document enhancement and binarization is the main pre-processing step in document analysis process. At first, a feed-forward denoising convolutional neural networks as a pre-processing methods for document image has been used for denoise images of additive white Gaussian noise(AWGN). The residual learning mechanism is used to learn the mapping from the noisy image to the residual image between the noisy image and the clean image in the neural network training process. A median filtering has been employed for denoising 'salt and pepper' noise. Given the learned denoising and enhanced image, we compute the adaptive threshold image using local adaptive threshold algorithm and then applies it to produce a binary output image. Experimental results show that combined those algorithms is robust in dealing with non-uniform illuminated, low contrast historic document images in terms of both accuracy and efficiency.
Cyberspace and Artificial Intelligence: The New Face of Cyber-Enhanced Hybrid Threats

While, until recently, cyber operations have constituted a specific subset of defense and security concerns, the synergization of cyberspace and artificial intelligence (AI), which are driving the Fourth Industrial Revolution, has raised the threat level of cyber operations, making them a centerpiece of what are called hybrid threats.The concept of hybrid threat is presently a key concern for the defense and security community; cyber-enabled and cyber-enhanced hybrid operations have been amplified in scope, frequency, speed, and threat level due to the synergies that come from the use of cyberspace and machine learning (ML)-based solutions.In the present work, we address the relevance of cyberspace-based operations and artificial intelligence for the implementation of hybrid operations and reflect on what this cyber dimension of hybrid operations implies for the concept of what constitutes a cyberweapon, the concept of hybrid human intelligence (hybrid HUMINT) and possible responses to the hybrid threat patterns.