Reviewerly/exHarmony huggingface dataset:
ITGH: Information-Theoretic Granger Causal Inference on Heterogeneous Data

Granger causality for time series states that a cause improves the predictability of its effect. That is, given two time series x and y, we are interested in detecting the causal relations among them considering the previous observations of both time series. Although, most of the algorithms are designed for causal inference among homogeneous processes where only time series from a specific distribution (mostly Gaussian) are given, many applications generate a mixture of various time series from different distributions. We utilize Generalized Linear Models (GLM) to propose a general information-theoretic framework for causal inference on heterogeneous data sets. We regard the challenge of causality detection as a data compression problem employing the Minimum Description Length (MDL) principle. By balancing the goodness-of-fit and the model complexity we automatically find the causal relations. Extensive experiments on synthetic and real-world data sets confirm the advantages of our algorithm ITGH (for Information-Theoretic Granger causal inference on Heterogeneous data) compared to other algorithms.
Scalable Anti-TrustRank with Qualified Site-level Seeds for Link-based Web Spam Detection

Web spam detection is one of the most important and challenging tasks in web search. Since web spam pages tend to have a lot of spurious links, many web spam detection algorithms exploit the hyperlink structure between the web pages to detect the spam pages. In this paper, we conduct a comprehensive analysis of the link structure of web spam using real-world web graphs to systemically investigate the characteristics of the link-based web spam. By exploring the structure of the page-level graph as well as the site-level graph, we propose a scalable site-level seeding methodology for the Anti-TrustRank (ATR) algorithm. The key idea is to map a website into a feature space where we learn a classifier to prioritize the websites so that we can effectively select a set of good seeds for the ATR algorithm. This seeding method enables the ATR algorithm to detect the largest number of spam pages among the competitive baseline methods. Furthermore, we design work-efficient asynchronous ATR algorithms which are able to significantly reduce the computational cost of the traditional ATR algorithm without degrading the performance in detecting spam pages while guaranteeing the convergence.
Multi-level Memory Network with CRFs for Keyphrase Extraction

Keyphrase, that concisely describe the high-level topics discussed in a document, are very useful for a wide range of natural language processing (NLP) tasks. Current popular supervised methods for keyphrase extraction commonly cannot effectively utilize the long-range contextual information in text. In this paper, we focus on how to effectively exploit the long-range contextual information to improve the keyphrase extraction performance. Specifically, we propose a multi-level memory network with the conditional random fields (CRFs), which allows to have unrestricted access to the long-range and local contextual information in text. We first design the multi-level memory network with sentence level and document level to enhance the text representation. Then, we integrate the multi-level memory network with the CRFs, which has an advantage in modeling the local contextual information. Compared with the recent state-of-the-art methods, our model can achieve better results through experiments on two datasets.
Guarantees on learning depth-2 neural networks under a data-poisoning attack

In recent times many state-of-the-art machine learning models have been shown to be fragile to adversarial attacks. In this work we attempt to build our theoretical understanding of adversarially robust learning with neural nets. We demonstrate a specific class of neural networks of finite size and a non-gradient stochastic algorithm which tries to recover the weights of the net generating the realizable true labels in the presence of an oracle doing a bounded amount of malicious additive distortion to the labels. We prove (nearly optimal) trade-offs among the magnitude of the adversarial attack, the accuracy and the confidence achieved by the proposed algorithm.
Neural Behavior-Based Approach for Neural Network Pruning

This paper presents a method for reducing the redundancy in both fully connected layers and convolutional layers of trained neural network models. The proposed method consists of two steps, 1) Neuro-Coding: to encode the behavior of each neuron by a vector composed of its outputs corresponding to actual inputs and 2) Neuro-Unification: to unify the neurons having the similar behavioral vectors. Instead of just pruning one of the similar neurons, the proposed method let the remaining neuron emulate the behavior of the pruned one. Therefore, the proposed method can reduce the number of neurons with small sacrifice of accuracy without retraining. Our method can be applied for compressing convolutional layers as well. In the convolutional layers, the behavior of each channel is encoded by its output feature maps, and channels whose behaviors can be well emulated by other channels are pruned and update the remaining weights. Through several experiments, we comfirmed that the proposed method performs better than the existing methods.
Validating the optimum tilt angle for PV modules in the highveld of South Africa for the summer season

Energy supply is a major problem in today's world due to increase in demand, depleting of fossil fuels and increase in global warming due to carbon emission.The need for an alternate, overall efficient and environment-friendly energy system has ascended globally.Photovoltaic (PV) systems can be used to harness solar energy that is considered to be one of the most promising alternative energy sources.However, its efficiency is affected not only by varying environmental conditions but also by the installation of its PV modules.The purpose of this paper is to empirically validate the optimum tilt for PV modules in the Highveld of South Africa.Three fixed-axis PV modules installed at optimum tilt angles of Latitude minus 10°, Latitude, and Latitude plus 10° serve as the basis of this study.These optimum tilt angles are utilised based on the recommendations by Heywood and Chinnery.A key recommendation is that PV modules should be mounted at Latitude minus 10° for the summertime period in the Highveld region of South Africa.
Order Matters: Shuffling Sequence Generation for Video Prediction

Predicting future frames in natural video sequences is a new challenge that is receiving increasing attention in the computer vision community. However, existing models suffer from severe loss of temporal information when the predicted sequence is long. Compared to previous methods focusing on generating more realistic contents, this paper extensively studies the importance of sequential order information for video generation. A novel Shuffling sEquence gEneration network (SEE-Net) is proposed that can learn to discriminate unnatural sequential orders by shuffling the video frames and comparing them to the real video sequence. Systematic experiments on three datasets with both synthetic and real-world videos manifest the effectiveness of shuffling sequence generation for video prediction in our proposed model and demonstrate state-of-the-art performance by both qualitative and quantitative evaluations. The source code is available at this https URL.
Face image barcodes by distributed cumulative histogram and clustering

Two generations of distributed cumulative histogram are in the base of the proposed approach to determine face features. The rough barcodes of the face image were obtained. The clustering K-means algorithm was applied to features to get more contrast bands of color. Then rough barcodes were transformed to real barcodes by highlighting the maximal bands and remaining all other as black background.
Editing in Style: Uncovering the Local Semantics of GANs

While the quality of GAN image synthesis has improved tremendously in recent years, our ability to control and condition the output is still limited. Focusing on StyleGAN, we introduce a simple and effective method for making local, semantically-aware edits to a target output image. This is accomplished by borrowing elements from a source image, also a GAN output, via a novel manipulation of style vectors. Our method requires neither supervision from an external model, nor involves complex spatial morphing operations. Instead, it relies on the emergent disentanglement of semantic objects that is learned by StyleGAN during its training. Semantic editing is demonstrated on GANs producing human faces, indoor scenes, cats, and cars. We measure the locality and photorealism of the edits produced by our method, and find that it accomplishes both.
Cooperative Detection of Multiple Targets by the Group of Mobile Agents

The paper considers the detection of multiple targets by a group of mobile robots that perform under uncertainty. The agents are equipped with sensors with positive and non-negligible probabilities of detecting the targets at different distances. The goal is to define the trajectories of the agents that can lead to the detection of the targets in minimal time. The suggested solution follows the classical Koopman's approach applied to an occupancy grid, while the decision-making and control schemes are conducted based on information-theoretic criteria. Sensor fusion in each agent and over the agents is implemented using a general Bayesian scheme. The presented procedures follow the expected information gain approach utilizing the "center of view" and the "center of gravity" algorithms. These methods are compared with a simulated learning method. The activity of the procedures is analyzed using numerical simulations.
A security specific knowledge modelling approach for secure software engineering

The Journal of the National Science Foundation of Sri Lanka publishes the results of research in all aspects of Science and Technology. The journal also has a website at http://www.nsf.gov.lk/. 2021 Impact Factor: 0.682The JNSF provides immediate open access to its content on the principle that making research freely available to the public supports a greater global exchange of knowledge.Cover :Leatherback (a), green (b - adult &amp; f - hatchling), hawksbill (c) and olive ridley (d) turtles who nest in Sri Lankan beaches, green turtle crawl marks (e) and by-catch of sea turtles in fisheries (c &amp; d)Photo credits: SJ Perera (a &amp; c), WPN Perera (b &amp; e), K Ekaratne (d &amp; f)
Streaming Object Detection for 3-D Point Clouds

Autonomous vehicles operate in a dynamic environment, where the speed with which a vehicle can perceive and react impacts the safety and efficacy of the system. LiDAR provides a prominent sensory modality that informs many existing perceptual systems including object detection, segmentation, motion estimation, and action recognition. The latency for perceptual systems based on point cloud data can be dominated by the amount of time for a complete rotational scan (e.g. 100 ms). This built-in data capture latency is artificial, and based on treating the point cloud as a camera image in order to leverage camera-inspired architectures. However, unlike camera sensors, most LiDAR point cloud data is natively a streaming data source in which laser reflections are sequentially recorded based on the precession of the laser beam. In this work, we explore how to build an object detector that removes this artificial latency constraint, and instead operates on native streaming data in order to significantly reduce latency. This approach has the added benefit of reducing the peak computational burden on inference hardware by spreading the computation over the acquisition time for a scan. We demonstrate a family of streaming detection systems based on sequential modeling through a series of modifications to the traditional detection meta-architecture. We highlight how this model may achieve competitive if not superior predictive performance with state-of-the-art, traditional non-streaming detection systems while achieving significant latency gains (e.g. 1/15'th - 1/3'rd of peak latency). Our results show that operating on LiDAR data in its native streaming formulation offers several advantages for self driving object detection -- advantages that we hope will be useful for any LiDAR perception system where minimizing latency is critical for safe and efficient operation.
LOVBench: Ontology Ranking Benchmark

Ontology search and ranking are key building blocks to establish and reuse shared conceptualizations of domain knowledge on the Web. However, the effectiveness of proposed ontology ranking models is difficult to compare since these are often evaluated on diverse datasets that are limited by their static nature and scale. In this paper, we first introduce the LOVBench dataset as a benchmark for ontology term ranking. With inferred relevance judgments for more than 7000 queries, LOVBench is large enough to perform a comparison study using learning to rank (LTR) with complex ontology ranking models. Instead of relying on relevance judgments from a few experts, we consider implicit feedback from many actual users collected from the Linked Open Vocabularies (LOV) platform. Our approach further enables continuous updates of the benchmark, capturing the evolution of ontologies’ relevance in an ever-changing data community. Second, we compare the performance of several feature configurations from the literature using LOVBench in LTR settings and discuss the results in the context of the observed real-world user behavior. Our experimental results show that feature configurations which are (i) well-suited to the user behavior, (ii) cover all features types, and (iii) consider decomposition of features can significantly improve the ranking performance.
Publishing and using record-keeping structural information in a blockchain

Purpose This paper aims to examine the role blockchain can play for record-keeping by exploring what information from a record-keeping system it is possible to publish to a blockchain. A credible approach is presented, followed by a discussion on both benefits and limitations. Design/methodology/approach The approach is a combination of theorised possibilities verified with practical software implementation. The basis for the work is relevant record-keeping and blockchain literature. Findings The results show that it is possible to separate the formal record keeping structure from content, and this opens for new possibilities when integrating record keeping and block chain technologies. However, the approach does come with some limitations. Research limitations/implications The approach is beneficial where there is a record-keeping standard that has a clearly defined metadata model, and that also makes use of globally unique identifiers. Privacy legislation, for example, GDPR, may limit the scope of an implementation of the approach. Originality/value The originality lies in presenting an approach whereby a record-keeping standard is analysed, separating structural and content information to publish structural information to a blockchain.
Obtaining Faithful Interpretations from Compositional Neural Networks

Neural module networks (NMNs) are a popular approach for modeling compositionality: they achieve high accuracy when applied to problems in language and vision, while reflecting the compositional structure of the problem in the network architecture. However, prior work implicitly assumed that the structure of the network modules, describing the abstract reasoning process, provides a faithful explanation of the model's reasoning; that is, that all modules perform their intended behaviour. In this work, we propose and conduct a systematic evaluation of the intermediate outputs of NMNs on NLVR2 and DROP, two datasets which require composing multiple reasoning steps. We find that the intermediate outputs differ from the expected output, illustrating that the network structure does not provide a faithful explanation of model behaviour. To remedy that, we train the model with auxiliary supervision and propose particular choices for module architecture that yield much better faithfulness, at a minimal cost to accuracy.
Robustness of continuous non‐smooth finite‐time Lyapunov control for two‐level quantum systems

The robustness of continuous non-smooth Lyapunov control for two-level quantum systems with uncertainties in the Hamiltonians is analysed in this study. The authors first transform a perturbed two-level system into a real-variable system by expressing the quantum state in terms of its complex exponential and then present two theorems to describe the behaviour of solutions of the perturbed system after a certain finite time by combining the Lyapunov and converse Lyapunov finite-time stability theorems with the homogeneity theory. Numerical simulation experiments on a spin- particle system show the effectiveness of the results obtained in this study.
On a conjecture about maximum scattered subspaces of $\mathbb{F}_{q^6}\times \mathbb{F}_{q^6}$

Maximum scattered subspaces are not only objects of intrinsic interest in finite geometry but also powerful tools for the construction of MRD-codes, projective two-weight codes, and strongly regular graphs. In 2018 Csajbók, Marino, Polverino, and Zanella introduced a new family of maximum scattered subspaces in $\mathbb{F}_{q^6} \times \mathbb{F}_{q^6}$ arising from polynomials of type $f_b(x)=bx^q+x^{q^4}$ for certain choices of $b \in \mathbb{F}_{q^6}$. Throughout characterizations for $f_{b_2}(x)$ and $f_{b_1}(x)$ giving rise to equivalent maximum scattered subspaces, the authors conjectured that the portion of new and inequivalent maximum scattered subspaces obtained in this way is quite large. In this paper first we find necessary and sufficient conditions for $b$ to obtain a maximum scattered subspace. Such conditions were found independently with different techniques also by Polverino and Zullo 2019. Then we prove the conjecture on the number of new and inequivalent maximum scattered subspaces of this family.
Centaur: A Chiplet-based, Hybrid Sparse-Dense Accelerator for Personalized Recommendations

Personalized recommendations are the backbone machine learning (ML) algorithm that powers several important application domains (e.g., ads, e-commerce, etc) serviced from cloud datacenters. Sparse embedding layers are a crucial building block in designing recommendations yet little attention has been paid in properly accelerating this important ML algorithm. This paper first provides a detailed workload characterization on personalized recommendations and identifies two significant performance limiters: memory-intensive embedding layers and compute-intensive multi-layer perceptron (MLP) layers. We then present Centaur, a chiplet-based hybrid sparse-dense accelerator that addresses both the memory throughput challenges of embedding layers and the compute limitations of MLP layers. We implement and demonstrate our proposal on an Intel HARPv2, a package-integrated CPU+FPGA device, which shows a 1.7-17.2x performance speedup and 1.7-19.5x energy-efficiency improvement than conventional approaches.
A global comparison of corporate value adjustments to news of cyber-attacks

The growing threat of cyber breach has become one of the most feared risks corporations around the world are currently dealing with. This paper uses a methodology similar to Hogan, Olson, and Angelina (2020) to analyze global shareholder value effects of cyber breaches from 1990 to 2019 for five major non-US countries. Cumulative Average Returns (CARs) are calculated using the first notice date to periods of up to 90 days post-announcement to compare short-term and long-term effects of cyber breaches on the stock price. Results for this data set show significant negative returns for US corporations in all windows. Unlike its US counterparts, short-term results for non-US countries show no significant changes to price as a result of cyber breach announcements. Long-term results for the aggregate non-US sample show significance only at the (0,30) window. Individual country long-term analysis shows some significance depending on the event windows, but no common patterns are seen among countries. These results point to differences in how news of a cyber breach, by country, is perceived in the market. The results help explain some of the patterns insurance companies have seen in the reticent buying habits of global companies with respect to cyber insurance.
Rational functions of Degree Four that Permute the Projective Line over a Finite Field

Recently, rational functions of degree three that permute the projective line $\Bbb P^1(\Bbb F_q)$ over a finite field $ \Bbb F_q$ were determined by Ferraguti and Micheli. In the present paper, using a different method, we determine all rational functions of degree four that permute the $\Bbb P^1(\Bbb F_q)$.
Building an Unmanned Aerial Vehicle for Humanitarian Aid Delivery

When an emergency occurs, immediate responses are needed for the affected areas. Conventional means of transportation often face difficulties carrying supplies to the location in need, especially in severely damaged areas.In this project, a small semi-autonomous unmanned aerial vehicle (UAV) was designed and constructed to undertake a representative humanitarian aid mission. The system has a novel hex-rotor design with overlapping propellers. Half of the motors have been turned upside down with a slight overlap between each propeller, hence reducing the total dimension of the system without losing its efficiency. The system is also equipped with a new lightweight servo activated two ring release system, allowing it to carry and release two kilograms of payloads with only small physical force needed.Overall, the system is capable to operate automatically while performing a series of tasks such as navigate waypoints and deliver payloads before returning to base. the system created in this project could deliver two individual payloads of 1 kg each to the pre-destinated target almost autonomously. The system provided promising results that encourage the utilization of UAV for delivering Humanitarian Aid.
Materialization of OWL Ontologies from Relational Databases: A Practical Approach

Providing both end-users and applications with a uniform way to query legacy databases through a high-level ontology that models both the business logic and the underlying data sources is the main concern in Ontology-based Data Access (OBDA). Our goal in this research is providing tools for performing OBDA with relational and non-relational data sources. Within the OBDA framework, in this work, we present a prototype tool that can access an H2 database, allowing the user to explicitly express mappings, and populating an ontology that can be saved for later querying. We report on the current functionality of our tool, which includes creating, loading, saving a global ontology populated with a database or a CSV file. For the latter, we devised a language for specifying the underlying schema of the CSV file. We argue that this language is better suited than current alternatives such as JSON. Also, the system allows the user to visually express mappings from the database to the ontology and the ability to create databases for testing the behavior of the system in the presence of increasing workloads. Our tests indicate that the system can handle a moderate workload of tables of tens of thousands of records but fails to handle tables of millions of records.