Reviewerly/exHarmony huggingface dataset:
Building a Heuristic for Greedy Search

Suboptimal heuristic search algorithms such as greedy best-first search allow us to find solutions when constraints of either time, memory, or both prevent the application of optimal algorithms such as A*. Guidelines for building an effective heuristic for A* are well established in the literature, but we show that if those rules are applied for greedy best-first search, performance can actually degrade. Observing what went wrong for greedy best-first search leads us to a quantitative metric appropriate for greedy heuristics, called Goal Distance Rank Correlation (GDRC). We demonstrate that GDRC can be used to build effective heuristics for greedy best-first search automatically.
Known Sample Attacks on Relation Preserving Data Transformations

Many data mining applications such as clustering and k-NN search rely on distances and relations in the data. Thus, distance preserving transformations, which perturb the data but retain records' distances, have emerged as a prominent privacy protection method. In this paper, we present a novel attack on a generalized form of distance preserving transformations, called relation preserving transformations. Our attack exploits not the exact distances between data, but the relationships between the distances. We show that an attacker with few known samples (4 to 10) and direct access to relations can retrieve unknown data records with more than 95 percent precision. In addition, experiments demonstrate that simple methods of noise addition or perturbation are not sufficient to prevent our attack, as they decrease precision by only 10 percent.
Application of a software agility assessment model – AgilityMod in the field

Adoption of agile values and principles and transformation of organizations towards agility are not easy and straightforward. Misinterpretation of agile principles and values, and adoption of partial solutions with few agile practices instead of holistic approaches prevent organizations to obtain full benefits of agile methods. We developed the Software Agility Assessment Reference Model (AgilityMod) for the appraisal of software projects from agility perspective and to provide guidance on specifying gaps on the road towards agility (agile maturity). The meta-model of AgilityMod was defined in relation with the ISO/IEC 15504-Process Assessment Model. AgilityMod was developed in an iterative and incremental manner by running successive case studies and getting opinions of experts for the evaluation and improvement of the Model. The multiple case study that we present here in detail included the implementation of the Model in eight software development companies. The results of this case study were evaluated by the case study participants. According to the significant majority of the case study participants, AgilityMod achieves its purpose.
Security of NoSQL Database Against Intruders

Background: The evolution of distributed web-based applications and cloud computing has brought about the demand to store a large amount of big data in distributed databases. Such efficient systems offer excessive availability and scalability to users. The new type of database resolves many new challenges especially in large-scale and high concurrency applications which are not present in the relational database. NoSQL refers to non-relational databases that are different from the Relational Database Management System. Objective: NoSQL has many features over traditional databases such as high scalability, distributed computing, lower cost, schema flexibility, semi or un-semi structural data and no complex relationship. Method: NoSQL databases are “BASE” Systems. The BASE (Basically Available, Soft state, Eventual consistency), formulates the CAP theorem the properties of which are used by BASE System. The distributed computer system cannot guarantee all of the following three properties at the same time that is consistency, availability and partition tolerance. Results: As progressively sharp big data is saved in NoSQL databases, it is essential to preserve higher security measures to ensure safe and trusted communication across the network. In this patent, we describe the security of NoSQL database against intruders which is growing rapidly. Conclusion: This patent also defines probably the most prominent NoSQL databases and describes their security aspects and problems.
A multi-feature probabilistic graphical model for social network semantic search

With the rapid development of social network platforms, more and more people are using them to search for material related to their interests. As the texts of social media messages are usually so short, when traditional existing document modeling methods are used in social network search tasks, the problem of semantic sparsity arises, leading to low-quality semantic representation and low-precision social network search results. Fortunately, besides of short text, social media data also has other features, such as timestamps, locations, and its user information. In light of this, to realize precise social network search, we propose a multi-feature probabilistic graphical model (MFPGM), which can generate high-quality semantic representation. To deal with the problem of semantic sparsity, we exploit two strategies in MFPGM. First, we propose a concept named special region and utilize location information to aggregate short text into long text. Second, we introduce the biterm pattern that can generate dense semantic space by supposing that a biterm occurring in the same context has the same topic. In order to generate high-quality semantic representations, we simultaneously model multiple features (i.e., biterm, user, location and timestamp) of social network data to enhance the semantic learning process of MFPGM. We conduct a lot of experiments on real-word datasets, and the comparisons with several state-of-art baseline methods have demonstrated the superiority of our MFPGM on topic quality and search performance. Additionally, with the help of the generated semantic representations, MFPGM allows people to analyze the relationships between time and the popularities of topics.
Optimal planning of the joint placement of photovoltaic panels and green roofs under climate change uncertainty

Photovoltaic (PV) panels directly convert sunlight into electricity; but, sunlight also heats the panels, negatively impacting their efficiency. Green roofs are vegetative layers grown on rooftops, mainly to provide added insulation on the roof to save energy. Green roofs also cool near-surface air temperature. Hence, the joint installation of PV panels and green roofs may potentially lead to higher efficiency of PV panels in certain climates. We develop a two-stage stochastic programming model to optimally place PV panels and green roofs under climate change uncertainty to maximize the overall profit from energy generated and saved. We calibrate the model using the literature, industry reports, and the data from different, at times conflicting, climate projections. We then conduct a case study for a mid-size city in the U.S., perform extensive sensitivity and robustness analyses and provide insights.
Dynamic Space-Time Scheduling for GPU Inference.

Serving deep neural networks in latency critical interactive settings often requires GPU acceleration. However, the small batch sizes typical in online inference results in poor GPU utilization, a potential performance gap which GPU resource sharing can address. In this paper, we explore several techniques to leverage both temporal and spatial multiplexing to improve GPU utilization for deep learning inference workloads. We evaluate the performance trade-offs of each approach with respect to resource-efficiency, latency predictability, and isolation when compared with conventional batched inference. Our experimental analysis suggests up to a 5x potential for improved utilization through the exploration of more advanced spatial and temporal multiplexing strategies. Our preliminary prototype of a dynamic space-time scheduler demonstrates a 3.23x floating-point throughput increase over space-only multiplexing and a 7.73x increase over time-only multiplexing for convolutions, while also providing better isolation and latency predictability.
Robust visual tracking via Laplacian Regularized Random Walk Ranking

Visual tracking is a fundamental and important problem in computer vision and pattern recognition. Existing visual tracking methods usually localize the visual object with a bounding box. Recently, learning the patch-based weighted features has been demonstrated to be an effective way to mitigate the background effects in the target bounding box descriptions, and can thus improve tracking performance significantly. In this paper, we propose a simple yet effective approach, called Laplacian Regularized Random Walk Ranking (LRWR), to learn more robust patch-based weighted features of the target object for visual tracking. The main advantages of our LRWR model over existing methods are: (1) it integrates both local spatial and global appearance cues simultaneously, and thus leads to a more robust solution for patch weight computation; (2) it has a simple closed-form solution, which makes our tracker efficiently. The learned features are incorporated into the structured SVM to perform object tracking. Experiments show that our approach performs favorably against the state-of-the-art trackers on two standard benchmark datasets.
Paradigms, possibilities, and probabilities: Comment on Hinterecker, Knauff, and Johnson-Laird (2016).

Hinterecker, Knauff, and Johnson-Laird (2016) compared the adequacy of the probabilistic new paradigm in reasoning with the recent revision of mental models theory (MMT) for explaining a novel class of inferences containing the modal term possibly. For example, the door is closed or the window is open or both, therefore, possibly the door is closed and the window is open (A or B or both, therefore, possibly(A & B)). They concluded that their results support MMT. In this comment, it is argued that Hinterecker et al. (2016) have not adequately characterized the theory of probabilistic validity (p-validity) on which the new paradigm depends. It is unclear how p-validity can be applied to these inferences, which are anyway peripheral to the theory. It is also argued that the revision of MMT is not well motivated and its adoption leads to many logical absurdities. Moreover, the comparison is not appropriate because these theories are defined at different levels of computational explanation. In particular, revised MMT lacks a provably consistent computational level theory that could justify treating these inferences as valid. It is further argued that the data could result from the noncolloquial locutions used to express the premises. Finally, an alternative pragmatic account is proposed based on the idea that a conclusion is possible if what someone knows cannot rule it out. This account could be applied to the unrevised mental model theory rendering the revision redundant. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
On the Qubit Routing Problem

We introduce a new architecture-agnostic methodology for mapping abstract quantum circuits to realistic quantum computing devices with restricted qubit connectivity, as implemented by Cambridge Quantum Computing's t|ket> compiler. We present empirical results showing the effectiveness of this method in terms of reducing two-qubit gate depth and two-qubit gate count, compared to other implementations.
Nonsense Attacks on Google Assistant and Missense Attacks on Amazon Alexa

This paper presents novel attacks on voice-controlled digital assistants using nonsensical word sequences. We present the results of a small-scale experiment which demonstrates that it is possible for malicious actors to gain covert access to a voice-controlled system by hiding commands in apparently nonsensical sounds of which the meaning is opaque to humans. Several instances of nonsensical word sequences were identified which triggered a target command in a voice-controlled digital assistant, but which were incomprehensible to humans, as shown in tests with human experimental subjects. Our work confirms the potential for hiding malicious voice commands to voice-controlled digital assistants or other speech-controlled devices in speech sounds which are perceived by humans as nonsensical. This paper also develops a novel attack concept which involves gaining unauthorised access to a voice-controlled system using apparently unrelated utterances. We present the results of a proof-of-co ncept study showing that it is possible to trigger actions in a voice-controlled digital assistant using utterances which are accepted by the system as a target command despite having a different meaning to the command in terms of human understanding.
Named Entity Recognition on Arabic-English Code-Mixed Data

As a result of globalization and better quality of education, a significant percentage of the population in Arab countries have become bilingual/multilingual. This has raised to the frequency of code-switching and code-mixing among Arabs in daily communication. Consequently, huge amount of Code-Mixed (CM) content can be found on different social media platforms. Such data could be analyzed and used in different Natural Language Processing (NLP) tasks to tackle the challenges emerging due to this multilingual phenomenon. Named Entity Recognition (NER) is one of the major tasks for several NLP systems. It is the process of identifying named entities in text. However, there is a lack of annotated CM data and resources for such task. This work aims at collecting and building the first annotated CM Arabic-English corpus for NER. Furthermore, we constructed a baseline NER system using deep neural networks and word embedding for Arabic-English CM text and enhanced it using a pooling technique.
Firearm Detection using Convolutional Neural Networks

This papers studies the application of the YOLO algorithm to create a firearm detection system, demonstrating its effectiveness in this task. We also constructed a dataset based on the website Internet Movie Firearm Database (IMFDB) for this study. Individuals carrying firearms in public places are a strong indicator of dangerous situations. Studies show that a rapid response from law enforcement agents is the main factor in reducing the number of victims. However, a large number of cameras to be monitored leads to an overload of CCTV operators, generating fatigue and stress, consequently, loss of efficiency in surveillance. Convolutional neural networks have been shown to be efficient in the detection and identification of objects in images, having sometimes produced more accurate and consistent results than human candidates.
Using E-Learning Tools to Enhance Students- Mathematicians’ Competences in the Context of International Academic Mobility Programmes

Introduction. The article is concerned with the use of special electronic teaching tools to increase the students’ understanding of the subject and adaptation to the professional language environment of the host country, taking into account the mathematical education. Our purpose is to develop a methodology of multilingual support of mathematical courses in the host country to improve the effectiveness of students’ academic mobility using e-learning tools. Materials and Methods. The basis of the research was methods of system analysis and descriptive and analytical methods, primarily experimental. To identify advantages of the proposed approach the methods of empirical research were used (observation and comparison). To prove the efficiency, classical methods of measurement were used. Results. We analyzed the existing electronic learning environments and defined an e-learning environment Math-Bridge that allows for creating mathematical courses in several languages in parallel. For example, the e-training course “Optimization Methods” was developed in three languages for training Russian-speaking Master programme students. The comparative analysis of the target and control student’s groups showed that 100 % of the students in the target group achieved an excellent level of mastering competencies, while the control group has only 75 %. For the control group, the degree of motivation to mathematical studying has not virtually changed (increase by 0,86 %). In the target group the level of student interest to the mathematics increased from 0,9 % to 8,9 % (mean 2.21 %). Discussion and Conclusion. The results described in the article will be useful for the staff of international departments, administrations and deans, as well as teachers of those universities that participate in the students’ international academic mobility programmes.
Prospection: Interpretable Plans From Language By Predicting the Future

High-level human instructions often correspond to behaviors with multiple implicit steps. In order for robots to be useful in the real world, they must be able to to reason over both motions and intermediate goals implied by human instructions. In this work, we propose a framework for learning representations that convert from a natural-language command to a sequence of intermediate goals for execution on a robot. A key feature of this framework is prospection, training an agent not just to correctly execute the prescribed command, but to predict a horizon of consequences of an action before taking it. We demonstrate the fidelity of plans generated by our framework when interpreting real, crowd-sourced natural language commands for a robot in simulated scenes.
A note on skew constacyclic codes over 𝔽q + u𝔽q + v𝔽q

In this paper, the skew constacyclic codes over finite non-chain ring [Formula: see text], where [Formula: see text], [Formula: see text] is an odd prime and [Formula: see text], are studied. We show that the Gray image of a skew [Formula: see text]-constacyclic code of length [Formula: see text] over [Formula: see text] is a skew quasi-twisted code of length [Formula: see text] over [Formula: see text] of index 3. Further, the structural properties of skew constacyclic codes over [Formula: see text] are obtained by the decomposition method.
Bayesian Generative Active Deep Learning

© 36th International Conference on Machine Learning, ICML 2019. All rights reserved. Deep learning models have demonstrated outstanding performance in several problems, but their training process tends to require immense amounts of computational and human resources for training and labeling, constraining the types of problems that can be tackled. Therefore, the design of effective training methods that require small labeled training sets is an important research direction that will allow a more effective use of resources. Among current approaches designed to address this issue, two are particularly interesting: data augmentation and active learning. Data augmentation achieves this goal by artificially generating new training points, while active learning relies on the selection of the most subset of unlabeled training samples to be labelled by an oracle. Although successful in practice, data augmentation can waste computational resources because it indiscriminately generates samples that are not guaranteed to be informative, and active learning selects a small subset of informative samples (from a large un-annotated set) that may be insufficient for the training process. In this paper, we propose a Bayesian generative active deep learning approach that combines active learning with data augmentation - we provide theoretical and empirical evidence (MNIST, CIFAR-{10,100}, and SVHN) that our approach has more efficient training and better classification results than data augmentation and active learning.
Low Gate-Count Ultra-Small Area Nano Advanced Encryption Standard (AES) Design

We present a low gate-count ultra-small area nano advanced encryption standard (AES) design. We achieve the low gate-count by the following means. First, we repeatedly reuse the area-critical circuits, i.e. one 8-bit Substitute-Box (S-Box) circuit and one 32-bit MixColumn circuit, for AES. Second, we cascade the input flip-flops (FFs) with our data transfer architecture so that the outputs of the MixColumn circuit are connected directly to the first 32-bit input FFs without extra multiplexing circuits. Third, the ShiftRow operation is implicitly performed by assigning the data sequence to the input FFs (during the S-Box and MixColumn operations). Fourth, we use independent XOR gates for AddRound and KeyExpansion operations. The collective means enables our design to feature 1457 gates, and to occupy 100um×100um area @ 65nm CMOS. When compared to the normalized area (@ 65nm CMOS) of the reported AES designs, our design features the smallest normalized area, 10% smaller than the most competitive reported AES design. Our design is targeted for ultra-small area applications including biomedical applications.
Sentiment Analysis of Cyberbullying on Instagram User Comments

Instagram is a social media for sharing images, photos and videos. Instagram has many active users from various circles. In addition to sharing submissions, Instagram users can also give likes and comments to other users' posts. However, the comment feature is often misused, for example it is used for cyberbullying which includes one act against the law. But until now, Instagram still does not provide a feature to detect cyberbullying. Therefore, this study aims to create a system that can classify comments whether they contain elements of cyberbullying or not. The results of the classification will be used to detect cyberbullying comments. The algorithm used for classification is Naïve Bayes Classifier. Then for each comment will pass the preprocessing and feature extraction stages with the TF-IDF method. For evaluation and testing using the K-Fold Cross Validation method. The experiment is divided into two, namely using stemming and without stemming. The training data used is 455 data. The best experimental results obtained an accuracy of 84% both with stemming, and without stemming.
Taking Defeasible Entailment Beyond Rational Closure

We present a systematic approach for extending the KLM framework for defeasible entailment. We first present a class of basic defeasible entailment relations, characterise it in three distinct ways and provide a high-level algorithm for computing it. This framework is then refined, with the refined version being characterised in a similar manner. We show that the two well-known forms of defeasible entailment, rational closure and lexicographic closure, fall within our refined framework, that rational closure is the most conservative of the defeasible entailment relations within the framework (with respect to subset inclusion), but that there are forms of defeasible entailment within our framework that are more “adventurous” than lexicographic closure.
Deep Gaussian Processes with Importance-Weighted Variational Inference

Deep Gaussian processes (DGPs) can model complex marginal densities as well as complex mappings. Non-Gaussian marginals are essential for modelling real-world data, and can be generated from the DGP by incorporating uncorrelated variables to the model. Previous work on DGP models has introduced noise additively and used variational inference with a combination of sparse Gaussian processes and mean-field Gaussians for the approximate posterior. Additive noise attenuates the signal, and the Gaussian form of variational distribution may lead to an inaccurate posterior. We instead incorporate noisy variables as latent covariates, and propose a novel importance-weighted objective, which leverages analytic results and provides a mechanism to trade off computation for improved accuracy. Our results demonstrate that the importance-weighted objective works well in practice and consistently outperforms classical variational inference, especially for deeper models.