Reviewerly/exHarmony huggingface dataset:
Towards Locally Differentially Private Generic Graph Metric Estimation

Local differential privacy (LDP) is an emerging technique for privacy-preserving data collection without a trusted collector. Despite its strong privacy guarantee, LDP cannot be easily applied to real-world graph analysis tasks such as community detection and centrality analysis due to its high implementation complexity and low data utility. In this paper, we address these two issues by presenting LF-GDPR, the first LDP-enabled graph metric estimation framework for graph analysis. It collects two atomic graph metrics - the adjacency bit vector and node degree - from each node locally. LF-GDPR simplifies the job of implementing LDP-related steps (e.g., local perturbation, aggregation and calibration) for a graph metric estimation task by providing either a complete or a parameterized algorithm for each step.
Multimodal Dependent Type Theory

We introduce MTT, a dependent type theory which supports multiple modalities. MTT is parametrized by a mode theory which specifies a collection of modes, modalities, and transformations between them. We show that different choices of mode theory allow us to use the same type theory to compute and reason in many modal situations, including guarded recursion, axiomatic cohesion, and parametric quantification. We reproduce examples from prior work in guarded recursion and axiomatic cohesion --- demonstrating that MTT constitutes a simple and usable syntax whose instantiations intuitively correspond to previous handcrafted modal type theories. In some cases, instantiating MTT to a particular situation unearths a previously unknown type theory that improves upon prior systems. Finally, we investigate the metatheory of MTT. We prove the consistency of MTT and establish canonicity through an extension of recent type-theoretic gluing techniques. These results hold irrespective of the choice of mode theory, and thus apply to a wide variety of modal situations.
Cheetah: Accelerating Database Queries with Switch Pruning

Modern database systems are growing increasingly distributed and struggle to reduce query completion time with a large volume of data. In this paper, we leverage programmable switches in the network to partially offload query computation to the switch. While switches provide high performance, they have resource and programming constraints that make implementing diverse queries difficult. To fit in these constraints, we introduce the concept of data pruning -- filtering out entries that are guaranteed not to affect output. The database system then runs the same query but on the pruned data, which significantly reduces processing time. We propose pruning algorithms for a variety of queries. We implement our system, Cheetah, on a Barefoot Tofino switch and Spark. Our evaluation on multiple workloads shows 40 - 200% improvement in the query completion time compared to Spark.
From Good Governance to Governance for Good: Blockchain for Social Impact

Abstract Agenda 2030 has triggered a phalanx of initiatives to expand the scale and scope of activities and actors addressing global challenges. One increasingly scrutinized area is the use of distributed ledger technologies (DLTs) in the fields of development and humanitarian aid. This paper examines three projects that have piloted DLT use cases, including cash‐based transfers to Syrian refugees by the World Food Programme; illegal child trafficking in Moldova by the United Nations Office for Project Services; and microlending to social enterprises in Brazil by Moeda. The paper explores the use of blockchain technology with a view to understanding its implications for governance. It argues that the decentralization and disintermediation associated with the blockchain are not a given outcome in the cases explored for this paper. Rather, the DLT projects under examination have centralizing tendencies that empower the state while replacing traditional intermediaries with new ones that have expertise in code and blockchain. The paper identifies limitations associated with DLT solutions and offers policy recommendations to enhance the use of DLTs to accelerate the achievement of the sustainable development goals. © 2020 John Wiley &amp; Sons, Ltd.
In-Ear EEG Based Attention State Classification Using Echo State Network

It is important to maintain attention when carrying out significant daily-life tasks that require high levels of safety and efficiency. Since degradation of attention can sometimes have dire consequences, various brain activity measurement devices such as electroencephalography (EEG) systems have been used to monitor attention states in individuals. However, conventional EEG instruments have limited utility in daily life because they are uncomfortable to wear. Thus, this study was designed to investigate the possibility of discriminating between the attentive and resting states using in-ear EEG signals for potential application via portable, convenient earphone-shaped EEG instruments. We recorded both on-scalp and in-ear EEG signals from 6 subjects in a state of attentiveness during the performance of a visual vigilance task. We have designed and developed in-ear EEG electrodes customized by modelling both the left and right ear canals of the subjects. We use an echo state network (ESN), a powerful type of machine learning algorithm, to discriminate attention states on the basis of in-ear EEGs. We have found that the maximum average accuracy of the ESN method in discriminating between attentive and resting states is approximately 81.16% with optimal network parameters. This study suggests that portable in-ear EEG devices and an ESN can be used to monitor attention states during significant tasks to enhance safety and efficiency.
Causal Relational Learning

Causal inference is at the heart of empirical research in natural and social sciences and is critical for scientific discovery and informed decision making. The gold standard in causal inference is performing randomized controlled trials ; unfortunately these are not always feasible due to ethical, legal, or cost constraints. As an alternative, methodologies for causal inference from observational data have been developed in statistical studies and social sciences. However, existing methods critically rely on restrictive assumptions such as the study population consisting of homogeneous elements that can be represented in a single flat table, where each row is referred to as a unit. In contrast, in many real-world settings, the study domain naturally consists of heterogeneous elements with complex relational structure, where the data is naturally represented in multiple related tables. In this paper, we present a formal framework for causal inference from such relational data. We propose a declarative language called CARL for capturing causal background knowledge and assumptions, and specifying causal queries using simple Datalog-like rules. CARL provides a foundation for inferring causality and reasoning about the effect of complex interventions in relational domains. We present an extensive experimental evaluation on real relational data to illustrate the applicability of CARL in social sciences and healthcare.
On Being Iterated: The Affective Demands of Design Participation

Iteration is a central feature of most HCI design methods, creating as it does opportunities for engagements with stakeholder groups. But what does iteration demand of those groups? Under what conditions do iterative engagements arise, and with what stakes? Building on experiences with Aboriginal Australian communities, and drawing on feminist and decolonial thinking, we examine the nature of iteration for HCI and how it frames encounters between design and use, with a focus on the affective dimension of engagement in iterative design processes.
CiwGAN and fiwGAN: Encoding information in acoustic data to model lexical learning with Generative Adversarial Networks

How can deep neural networks encode information that corresponds to words in human speech into raw acoustic data? This paper proposes two neural network architectures for modeling unsupervised lexical learning from raw acoustic inputs: ciwGAN (Categorical InfoWaveGAN) and fiwGAN (Featural InfoWaveGAN). These combine Deep Convolutional GAN architecture for audio data (WaveGAN; Donahue et al., 2019) with the information theoretic extension of GAN – InfoGAN (Chen et al., 2016) – and propose a new latent space structure that can model featural learning simultaneously with a higher level classification and allows for a very low-dimension vector representation of lexical items. In addition to the Generator and Discriminator networks, the architectures introduce a network that learns to retrieve latent codes from generated audio outputs. Lexical learning is thus modeled as emergent from an architecture that forces a deep neural network to output data such that unique information is retrievable from its acoustic outputs. The networks trained on lexical items from the TIMIT corpus learn to encode unique information corresponding to lexical items in the form of categorical variables in their latent space. By manipulating these variables, the network outputs specific lexical items. The network occasionally outputs innovative lexical items that violate training data, but are linguistically interpretable and highly informative for cognitive modeling and neural network interpretability. Innovative outputs suggest that phonetic and phonological representations learned by the network can be productively recombined and directly paralleled to productivity in human speech: a fiwGAN network trained on suit and dark outputs innovative start, even though it never saw start or even a [st] sequence in the training data. We also argue that setting latent featural codes to values well beyond training range results in almost categorical generation of prototypical lexical items and reveals underlying values of each latent code. Probing deep neural networks trained on well understood dependencies in speech bears implications for latent space interpretability and understanding how deep neural networks learn meaningful representations, as well as potential for unsupervised text-to-speech generation in the GAN framework.
Semantic Boundary Detection With Reinforcement Learning for Continuous Sign Language Recognition

Sign language recognition (SLR) is a significant and promising technique to facilitate the communication for the hearing-impaired people. In this paper, we are dedicated to weakly supervised continuous SLR, where for each sign video, there are only ordered gloss labels without temporal boundary along frames. To explicitly align video frames to the sign words in a sign video, we propose a novel semantic boundary detection method based on reinforcement learning for accurate continuous SLR. In our approach, we first propose a multi-scale perception scheme to learn discriminative representation for video clips. Then, we formulate the semantic boundary detection as a reinforcement learning problem. We define the state as the feature representation of a video segment, and the action as the determination of the semantic boundary's location. The reward is computed by the quantitative performance metric between the prediction sentence and the ground truth sentence. The policy network is trained with a policy gradient algorithm. Extensive experiments are conducted on CSL Split II and RWTH-PHOENIX-Weather 2014 datasets, and the results demonstrate the effectiveness and superiority of our method.
Reconciling enumerative and deductive program synthesis

Syntax-guided synthesis (SyGuS) aims to find a program satisfying semantic specification as well as user-provided structural hypotheses. There are two main synthesis approaches: enumerative synthesis, which repeatedly enumerates possible candidate programs and checks their correctness, and deductive synthesis, which leverages a symbolic procedure to construct implementations from specifications. Neither approach is strictly better than the other: automated deductive synthesis is usually very efficient but only works for special grammars or applications; enumerative synthesis is very generally applicable but limited in scalability.
Improved Image Processing Techniques for User Immersion Problem Alleviation in Virtual Reality Environments

The latest advertisements on the advancements of the virtual reality has paved way for diverse studies, in manifold fields that can benefit by utilizing the technologies of the virtual reality, not excluding the design, gaming and the simulated understanding. Yet whenever a virtual reality device conveys information in form of images with the assistance of the display that is positioned closer to the user’s eyes it faces problems like minimizing the speed of the process and degradation in the quality of images ending up in huge variations across the virtual realism and the realism causing user immersion problems. So to mitigate the immersion problems of the user because of the low quality of image and the minimization of processing speed in the virtual reality environments the paper puts forth an improved image processing technique to improvise the sharpness of the images in order to enhance quality of the images and heighten the processing speed.
An Efficient Optimization and Secured Triple Data Encryption Standard Using Enhanced Key Scheduling Algorithm

As a result of advancement in technology, the transfer rate of digital data through cryptographic embedded devices such as smart cards is increasing rapidly and these devices are vulnerable to attacks. Cryptography provides various algorithms to secure the data. Triple Data Encryption Standard (Triple-DES) algorithm [1] is one such and uses the Data Encryption Standard (DES) block thrice this reinforces the key size to 192 bits. The reliability of data can be made progressive by procuring the keys. In Triple-DES, the key generation does not undergo encryption, making the job of the cryptanalyst easy. A strategy must be followed to make the keys more reliable and avoid the glitches in the power traces to make it more strenuous for them. To satisfy the above requirement FORTIS algorithm has been proposed for generation of sub-keys and investigates the strength of the algorithm against side-channel power attacks using ChipWhisperer®-Lite and Artix FPGA as target board. The number of glitches that represent the leakage power are approximately reduced by 53.3% and from the power traces of key schedule algorithm it is seen that all the instructions being performed are similar, so it becomes difficult to identify which operation is being performed and the probability of guessing entropy has been reduced in 86.6% of the cases.
Institutions, Human Capital and Entrepreneurship Density

This study examines the impacts of national institutions and human capital on entrepreneurship density in a global sample of 67 economies over the period 2006–2016. Furthermore, the associations between human capital and institutions on entrepreneurial activities are investigated while controlling for financial development, economic growth, trade openness, FDI inflows and total natural rents. Employing the panel corrected standard error estimator to deal with cross-sectional dependence, we find that formal institutions represented by entrepreneurship procedures and the general institutional quality are positively associated with entrepreneurship density. Also, informal institutions represented by socialist history, common law system and colonial history are positively associated with entrepreneurship density. Notably, human capital has a moderating effect in reducing the negative impacts of weak institutions.
Optimal Detection of Phising Attack using SCA based K-NN

Phising is a dangerous social engineering cyber crime that aims to steal personal information of the user by sending spoofed emails. There are several applications of machine learning used to detect phising attacks. However, when a security model is built, the attacker tries to breach through it. Thus the question of optimal prediction of phising attack is still unanswerable. In this work, we integrated sine cosine algorithm, a metaheuristic population based technique with K-Nearest Neighbor (SCAK-NN) for predicting phising attacks optimally. The proposed SCAK-NN is compared with Decision Trees and Naïve Bayes. The results are satisfactory when comparing with various performance metrics such as accuracy, F-measure, True positive Rate (TPR), False Positive Rate (FPR) and Mean Absolute error (MAP) to hold that SCAK-NN is better than Decision Trees and Naïve Bayes.
Edge2vec

Graph embedding, also known as network embedding and network representation learning, is a useful technique which helps researchers analyze information networks through embedding a network into a low-dimensional space. However, existing graph embedding methods are all node-based, which means they can just directly map the nodes of a network to low-dimensional vectors while the edges could only be mapped to vectors indirectly. One important reason is the computational cost, because the number of edges is always far greater than the number of nodes. In this article, considering an important property of social networks, i.e., the network is sparse, and hence the average degree of nodes is bounded, we propose an edge-based graph embedding ( edge2vec ) method to map the edges in social networks directly to low-dimensional vectors. Edge2vec takes both the local and the global structure information of edges into consideration to preserve structure information of embedded edges as much as possible. To achieve this goal, edge2vec first ingeniously combines the deep autoencoder and Skip-gram model through a well-designed deep neural network. The experimental results on different datasets show edge2vec benefits from the direct mapping in preserving the structure information of edges.
Providing reliability in recommender systems through Bernoulli Matrix Factorization

Beyond accuracy, quality measures are gaining importance in modern recommender systems, with reliability being one of the most important indicators in the context of collaborative filtering. This paper proposes Bernoulli Matrix Factorization (BeMF), which is a matrix factorization model, to provide both prediction values and reliability values. BeMF is a very innovative approach from several perspectives: a) it acts on model-based collaborative filtering rather than on memory-based filtering, b) it does not use external methods or extended architectures, such as existing solutions, to provide reliability, c) it is based on a classification-based model instead of traditional regression-based models, and d) matrix factorization formalism is supported by the Bernoulli distribution to exploit the binary nature of the designed classification model. The experimental results show that the more reliable a prediction is, the less liable it is to be wrong: recommendation quality improves after the most reliable predictions are selected. State-of-the-art quality measures for reliability have been tested, which shows that BeMF outperforms previous baseline methods and models.
A public unified bug dataset for java and its assessment regarding metrics and bug prediction

Abstract Bug datasets have been created and used by many researchers to build and validate novel bug prediction models. In this work, our aim is to collect existing public source code metric-based bug datasets and unify their contents. Furthermore, we wish to assess the plethora of collected metrics and the capabilities of the unified bug dataset in bug prediction. We considered 5 public datasets and we downloaded the corresponding source code for each system in the datasets and performed source code analysis to obtain a common set of source code metrics. This way, we produced a unified bug dataset at class and file level as well. We investigated the diversion of metric definitions and values of the different bug datasets. Finally, we used a decision tree algorithm to show the capabilities of the dataset in bug prediction. We found that there are statistically significant differences in the values of the original and the newly calculated metrics; furthermore, notations and definitions can severely differ. We compared the bug prediction capabilities of the original and the extended metric suites (within-project learning). Afterwards, we merged all classes (and files) into one large dataset which consists of 47,618 elements (43,744 for files) and we evaluated the bug prediction model build on this large dataset as well. Finally, we also investigated cross-project capabilities of the bug prediction models and datasets. We made the unified dataset publicly available for everyone. By using a public unified dataset as an input for different bug prediction related investigations, researchers can make their studies reproducible, thus able to be validated and verified.
Medical Image Coloring Based on Gabor Filtering for Internet of Medical Things

Color medical images better reflect a patient's lesion information and facilitate communication between doctors and patients. The combination of medical image processing and the Internet has been widely used for clinical medicine on Internet of medical things. The classical Welsh method uses matching pixels to achieve color migration of grayscale images, but it exists problems such as unclear boundary and single coloring effect. Therefore, the key information of medical images after coloring can't be reflected efficiently. To address this issue, we propose an image coloring method based on Gabor filtering combined with Welsh coloring and apply it to medical grayscale images. By using Gabor filtering, which is similar to the visual stimulus response of simple cells in the human visual system, filtering in 4 directions and 6 scales is used to stratify the grayscale images and extract local spatial and frequency domain information. In addition, the Welsh coloring method is used to render the image with obvious textural features in the layered image. Our experiments show that the color transboundary problem can be solved effectively after the layered processing. Compared to images without stratification, the coloring results of the processed images are closer to the real image.
Nonlocal Weighted Robust Principal Component Analysis for Seismic Noise Attenuation

Seismic data are usually contaminated by various noises. Noise suppression plays an important role in seismic processing. In this article, we propose a new denoising method based on the nonlocal weighted robust principal component analysis (RPCA). First, seismic data are divided into many patches and grouped based on the nonlocal similarity. For each group, then, we establish a similar block matrix and set up the objective function of the RPCA. Next, we introduce the iterative log-thresholding algorithm into the augmented Lagrangian method to solve the problem. Furthermore, varying weights are specified to different singular values when minimizing the objective function. Finally, aggregating all recovered matrices can obtain the denoised seismic data. The proposed method considers the nonlocal similarity and adaptively sets weights with local noise variance. It performs well also owing to the superiority of the iterative log-thresholding method. The presented method is assessed using a synthetic seismic section with several crossover events. We also apply this novel approach to a real seismic data, which shows good results. Comparison with other approaches reveals the effectiveness of the proposed approach.