Reviewerly/exHarmony huggingface dataset:
A Group-Based NTRU-Like Public-Key Cryptosystem for IoT

The Internet of things have been widely used to improve our daily life. Public-key cryptosystem plays a central role in the security of IoT. However, classic public-key cryptosystems such as RSA and ECC are not suitable for IoT because of the complicated encryption and decryption operations. NTRU is an efficient public-key cryptosystem. In this paper, we first generalize NTRU and propose a group-based NTRU-like public-key cryptosystem, namely GTRU. Then, we construct high-performance GTRU for IoT. Finally, security analysis shows that our proposed GTRU for IoT is more secure than NTRU against lattice-based attacks.
A Survey of the General Public’s Views on the Ethics of Using AI in Education

Recent scandals arising from the use of algorithms for user profiling to further political and marketing gain have popularized the debate over the ethical and legal implications of using such ‘artificial intelligence’ in social media. The need for a legal framework to protect the general public’s data is not new, yet it is not clear whether recent changes in data protection law in Europe, with the introduction of the GDPR, have highlighted the importance of privacy and led to a healthy concern from the general public over online user tracking and use of data. Like search engines, social media and online shopping platforms, intelligent tutoring systems aim to personalize learning and thus also rely on algorithms that automatically profile individual learner traits. A number of studies have been published on user perceptions of trust in robots and computer agents. Unsurprisingly, studies of AI in education have focused on efficacy, so the extent of learner awareness, and acceptance, of tracking and profiling algorithms remains unexplored. This paper discusses the ethical and legal considerations for, and presents a case study examining the general public’s views of, AI in education. A survey was recently taken of attendees at a national science festival event highlighting state-of-the-art AI technologies in education. Whilst most participants (77%) were worried about the use of their data, in learning systems fewer than 8% of adults were ‘not happy’ being tracked, as opposed to nearly two-thirds (63%) of children surveyed.
Simultaneous Private Learning of Multiple Concepts

We investigate the direct-sum problem in the context of differentially private PAC learning: What is the sample complexity of solving $k$ learning tasks simultaneously under differential privacy, and how does this cost compare to that of solving $k$ learning tasks without privacy? In our setting, an individual example consists of a domain element $x$ labeled by $k$ unknown concepts $(c_1,\ldots,c_k)$. The goal of a multi-learner is to output $k$ hypotheses $(h_1,\ldots,h_k)$ that generalize the input examples. Without concern for privacy, the sample complexity needed to simultaneously learn $k$ concepts is essentially the same as needed for learning a single concept. Under differential privacy, the basic strategy of learning each hypothesis independently yields sample complexity that grows polynomially with $k$. For some concept classes, we give multi-learners that require fewer samples than the basic strategy. Unfortunately, however, we also give lower bounds showing that even for very simple concept classes, the sample cost of private multi-learning must grow polynomially in $k$.
High Resolution Sentiment Analysis by Ensemble Classification

This study proposes an approach to ensemble sentiment classification of a text to a score in the range of 1–5 of negative-positive scoring. A high-performing model is produced from TripAdvisor restaurant reviews via a generated dataset of 684 word-stems, gathered by information gain attribute selection from the entire corpus. The best performing classification was an ensemble classifier of RandomForest, Naive Bayes Multinomial and Multilayer Perceptron (Neural Network) methods ensembled via a Vote on Average Probabilities approach. The best ensemble produced a classification accuracy of 91.02% which scored higher than the best single classifier, a Random Tree model with an accuracy of 78.6%. Other ensembles through Adaptive Boosting, Random Forests and Voting are explored with ten-fold cross-validation. All ensemble methods far outperformed the best single classifier methods. Even though extremely high results are achieved, analysis documents the few mis-classified instances as almost entirely being close to their real class via the model’s given error matrix.
Collecting Preference Rankings Under Local Differential Privacy

In this paper, we initiate the study of collecting preference rankings under local differential privacy. The key technical challenge comes from the fact that the number of possible rankings increases factorially in the number of items to rank. In practical settings, this number could be large, leading to excessive injected noise. To solve this problem, we present a novel approach called SAFARI. The general idea is to collect a set of distributions over small domains which are carefully chosen based on the riffle independent model to approximate the overall distribution of users' rankings, and then generate a synthetic ranking dataset from the obtained distributions. By working on small domains instead of a large domain, SAFARI can significantly reduce the magnitude of added noise. Extensive experiments on real datasets confirm the effectiveness of SAFARI.
Inspection and Selection of Representations

We present a novel framework for inspecting representations and encoding their formal properties. This enables us to assess and compare the informational and cognitive value of different representations for reasoning. The purpose of our framework is to automate the process of representation selection, taking into account the candidate representation’s match to the problem at hand and to the user’s specific cognitive profile. This requires a language for talking about representations, and methods for analysing their relative advantages. This foundational work is first to devise a computational end-to-end framework where problems, representations, and user’s profiles can be described and analysed. As AI systems become ubiquitous, it is important for them to be more compatible with human reasoning, and our framework enables just that.
Simple methods to overcome the limitations of general word representations in natural language processing tasks

Although general word representations (GWRs) by skip-gram or GloVe have been widely used in many natural language processing (NLP) tasks with considerable success, they require further improvement. First, a GWR only represents general information of a word, even though task-oriented information can be more useful in specific tasks. Second, a GWR cannot avoid the out-of-vocabulary (OOV) problem. Thus, some recent studies have proposed methods based on an additional complex model or deep knowledge of resources for each specific task. Although such methods have the potential for improved performance, we believe that the baseline systems of each NLP task are already expensive; hence, making them more complex would be problematic for real-world applications. Therefore, the objective of this study is to overcome the limitations of GWRs by developing simple but effective methods for task-specific word representations (TSWRs) and OOV representations (OOVRs). The proposed methods achieved state-of-the-art performance in four Korean NLP tasks, namely part-of-speech tagging, named entity recognition, dependency parsing, and semantic role labeling.
Paths of economic development: A global evidence for the mediating role of human capital

This paper revisits the roles of institutions and human capital in the development process by using structural equation modeling with a latent construct. Two models are constructed by using the data of 143 countries with 14 publicly available indicators; non-mediated (Model A) and mediated one (Model B). A path between institutional quality and economic development is identified in Model A and found as significant. When human capital is added into the Model B as a mediator, the direct relationship between institutional quality and economic development which is confirmed in Model A becomes insignificant. This evidence contributes to the debate by explaining the roles of institutions and human capital in the development process, based on the existing level of institutional quality that determines conditions on decisions for starting or sustaining the development process. In other words, (a) improving institutions in addition to human capital is needed for the countries with low level of institutional quality to start development process and; (b) on the other hand, since the human capital develops immunity on the quality of the institutions, human capital plays a more basic role to sustain the development process for the countries with a high level of institutional quality.
DREAM

Traffic consolidation has been proposed to save energy in data center networks. However, existing centralized traffic consolidation approaches focus on achieving optimal network energy saving, without considering the need to be responsive to traffic variability. In this paper, we present DREAM, a distributed flowlet-level traffic consolidation framework for achieving energy efficiency in data center networks. DREAM splits a TCP flow across multiple paths based on ECN feedback by adapting the path selection probability for sending a flowlet. This helps to choose a path while avoid congestion/queue build-up at switches. Distributed agents in DREAM are implemented in Open vSwitch at each server without modifying applications, TCP, or the hardware switches in the data center network. Testbed evaluations using traces from Wikipedia web service and Facebook MapReduce traffic prove that DREAM on average achieves at least 15.8% energy saving for the data center network, while state-of-the-art approaches such as CARPO and ElasticTree produce 11.6% and 8.4% energy saving, respectively. The packet drop ratio in DREAM is less than 0.01% while the best among the alternatives, ElasticTree, has at least 0.19% drop ratio. DREAM also has 30% lower application-level latency than state-of-the-art centralized traffic consolidation approaches.
Strikes-Thrusts Activity Recognition Using Wrist Sensor Towards Pervasive Kendo Support System

In this paper, we focus on Kendo, which is a traditional sport in Japan, and propose a strikes-thrusts activity recognition method using a wrist sensor towards a pervasive Kendo support system. We collected the inertial sensor data set from 6 subjects. We attached 3 inertial sensor units (IMUs) on the subjects body, and 2 IMUs on the Shinai (bamboo sword used for Kendo). On the body, IMUs were placed on the Right Wrist, Waist and Right Ankle. On the Shinai, they were placed on the Tsuba and Saki-Gawa. We first classified strikes-thrusts activities consisting of 4 general types, Men, Tsuki, Do, and Kote, followed by further classification into 8 detailed types. We achieved 90.0% of F-measure in the case of 4-type classification and 82.6% of F-measure in the case of 8-type classification when learning and testing the same subjects data for only Right Wrist. Further, when adding data of sensors attached to the Waist and Right Ankle, we achieved 97.5% of F-measure for 4-type classification and 91.4% of F-measure for 8-type classification. As a result of leave-one-person-out cross-validation from 6 subjects to confirm generalized performance, in the case of 4-type classification, we achieved 77.5% of F-measure by using only 2 IMUs (Right Wrist and Shinai Tsuba).
TF2FPGA: A Framework for Projecting and Accelerating Tensorflow CNNs on FPGA Platforms

FPGA-based accelerators of Convolutional Neural Networks (CNNs) deliver significant performance, while leaving much room for optimizations. In this paper, we present a CNN accelerator based on the state-of-the-art Dataflow Hardware Mapping (DHM) methodology, optimized with the use of 8-bit unsigned integer quantization, 1-bit input mapping and deep-to-shallow conversion techniques. Interestingly, we introduce a Tensorflow-to-VHDL framework to generate high-performance accelerators for inferencing on FPGAs, adopting a holistic view of CNN problems. The proposed framework uses a VHDL library of our pre-optimized layers and modules, and creates the accelerator by extracting the model definition and weights from the respective Tensorflow files. The 8-bit quantized weights are stored in an on-board ROM memory, and thus, the need for external-to-FPGA “glue logic” modules and increased bandwidth is eliminated. Additionally, we include a Matrix Multiplication-to-FIR conversion method that manages to include the fully connected part of the CNN inside the FPGA. Finally, we achieve 75% less memory footprint and 53% less resource utilization compared to the state-of-the-art based architectures, and up to ×10 speedup vs different GPU, CPU combinations.
Socially Responsive eCommerce Platforms

Before the advent of internet technologies, African merchants have always had indigenous none techno-driven strategies with which they use to engage, maintain and satisfy customers within their communities. However, due to the cross-border marketing/economic opportunities that the internet provides, many African merchants are setting up eCommerce sites for their businesses. Research shows that successful eCommerce sites are operationalized with various persuasive techniques to promote customer engagement and improve user experiences. This is also true for African based eCommerce sites. Various researchers have evaluated and compared persuasive techniques operationalized on indigenous African eCommerce sites. However, none of those studies sought to understand the implications of the age-long traditional marketing strategies in the design of African based eCommerce platforms. Therefore, this paper analyzes the persuasive techniques employed in conventional African marketplaces to uncover the design requirements that could be operationalized on the eCommerce version. To achieve this objective, we conducted a mixed method study on 151 participants. We conducted qualitative studies (comprising interviews, observations, and conceptual investigations) on 50 African merchants to uncover the techniques they use to attract, satisfy and retain their customers in the conventional market. Secondly, we conducted quantitative studies on 101 customers to uncover the effects of those techniques on them. Among other things, the results from the studies revealed various techniques, which traditional African merchants use to attract and retain customers. The results also revealed the effects of those techniques on customers' purchasing behaviors. We offer design guidelines to operationalize and tailor those techniques to attract, recruit, satisfy, retain and promote repeated purchases on eCommerce that target customers from developing countries.
Local Graph Edge Partitioning with a Two-Stage Heuristic Method

Graph edge partitioning divides the edges of an input graph into multiple balanced partitions of a given size to minimize the sum of vertices that are cut, which is critical to the performance of distributed graph computation platforms. Existing graph partitioning methods can be classified into two categories: offline graph partitioning and streaming graph partitioning. The first category requires global information for a graph during the partitioning, which is expensive in terms of time and memory for large-scale graphs. The second category, however, creates partitions solely based on the received edge information, which may result in lower performance than the offline methods. Therefore, in this study, the concept of local graph partitioning is introduced from local community detection to consider only local information, i.e., a part of the graph, instead of the graph as a whole, during the partitioning. The characteristic of storing only local information is important because real-world graphs are often large in scale, or they increase incrementally. Based on this idea, we propose a two-stage local partitioning algorithm, where the partitioning process is divided into two stages according to the structural changes of the current partition, and two different strategies are introduced to deal with the respective stages. Experimental results with real-world graphs demonstrate that the proposed algorithm outperforms the rival algorithms in most cases, including the state-of-the-art algorithm METIS.
Deep Semantic Space with Intra-class Low-rank Constraint for Cross-modal Retrieval

In this paper, a novel Deep Semantic Space learning model with Intra-class Low-rank constraint (DSSIL) is proposed for cross-modal retrieval, which is composed of two subnetworks for modality-specific representation learning, followed by projection layers for common space mapping. In particular, DSSIL takes into account semantic consistency to fuse the cross-modal data in a high-level common space, and constrains the common representation matrix within the same class to be low-rank, in order to induce the intra-class representations more relevant. More formally, two regularization terms are devised for the two aspects, which have been incorporated into the objective of DSSIL. To optimize the modality-specific subnetworks and the projection layers simultaneously by exploiting the gradient decent directly, we approximate the nonconvex low-rank constraint by minimizing a few smallest singular values of the intra-class matrix with theoretical analysis. Extensive experiments conducted on three public datasets demonstrate the competitive superiority of DSSIL for cross-modal retrieval compared with the state-of-the-art methods.
Multi-feature based Question–Answerer Model Matching for predicting response time in CQA

Users of Community Question Answering (CQA) could not manage their time conveniently because their questions are often not answered quickly enough. To address this problem, we try to provide a function for CQA sites to inform users when their questions will be answered. In this paper, we propose a Question–Answerer Model Matching based answerer's response time prediction named (QAM2), which consists of two parts: the construction of the Multi-feature based Question–Answerer Model (MQAM, including the answerer model and the question model) and the prediction of question response time based on MQAM Matching Strategy (QAMMS). Firstly, the MQAM is built according to some extracted deep features (e.g., answerer's interest, professional level, activity, question category and difficulty), which are neglected in most existing methods on the prediction of question response time. Herein, the Label Cluster Latent Dirichlet Allocation (LC-LDA) model was proposed to overcome the compulsive allocation behaviors caused by traditional topic models (e.g. LDA), which treats the words that are irrelevant or weakly related to the subject as the topic of short texts when extracting the feature of answerer's interest and question category. Meanwhile, an improved PageRank algorithm-topic sensitive weighted PageRank (TSWPR) is used to eliminate the impact of "indiscriminate" users who have answered many questions with low quality of answers. Secondly, we use the model matching strategy based on multiple classifier for matching MQAM and calculating the question response time of each answerer. Experiments conducted on two real data sets of Stack Overflow show that the proposed method can improve significantly the accuracy of question response time prediction in CQA.
AsterixDB Mid-Flight: A Case Study in Building Systems in Academia

Building large software systems is always a challenging venture, but it is especially so in academia. This paper describes the experiences that the author and his (mostly UC-based) partners in software crime have had that culminated in the Big Data Management System now available as Apache AsterixDB. It covers a mix of the history and technical content of the nearly ten-year-old project, starting with its inception during the MapReduce craze. It describes the phases that the effort has gone through and some of the lessons learned along the way. The paper also covers some personal reflections and opinions about the challenges of systems-building, as well as writing about it, in our current academic culture. Included is the case for doing this sort of work at all - discussing the pitfalls of doing "systems" research in the absence of an actual system, and why the gain outweighs the pain of building and sharing database software in academia. As of late 2018, Apache AsterixDB is also having a commercial impact as the storage and parallel query engine underlying a new offering called Couchbase Analytics. The last part of the paper explains how we are attempting to balance the uses of AsterixDB as (i) a generally available open source Apache software platform, (ii) an end-to-end research testbed for universities, and (iii) the technology powering a commercial NoSQL product.
An Efficient Method for Quantum Circuit Placement Problem on a 2-D Grid

This work-in-progress report proposes an efficient method to solve "quantum circuit placement problems" which are considered explicitly or implicitly when we convert an arbitrary quantum circuit into a nearest neighbor (NN) compliant circuit. A quantum circuit placement problem is to find as few SWAP gates as possible to convert a given initial qubit placement into a desired qubit placement. In the existing methods, the problem is solved by an ILP formulation or an A* search algorithm; the existing approaches may not be scalable for large quantum circuits. Thus, we are considering a more efficient method to solve the problem; our method tries to apply only SWAP gates such that do not have any bad effect on the desired movement of all the qubits. We also report a preliminary experimental result to show how our method improve the A* search algorithm which is used to generate NN compliant circuits.
Cultural heritage visits supported on visitors’ preferences and mobile devices

Monuments, museums and cities are great places to feel and experience neat and interesting things. But cultural heritage is experienced differently by different visitors. The more erudite may know beforehand what they intend to explore, while the least literate usually know and are capable of expressing some of their preferences but do not exactly realize what to see and explore. This paper proposes the use of a mobile application to set an itinerary where you can move at your own pace and, at the same time, have all the complementary information you need about each of the points of interest. The application is designed in face of an adaptive user interface where the routing and augmented reality are connected to acknowledge the needs of different user categories, such as elders, kids, experts or general users
Accurate salient object detection via dense recurrent connections and residual-based hierarchical feature integration

Recently, the convolutional neural network (CNN) has achieved great progress in many computer vision tasks including object detection, image restoration, and scene understanding. In this paper, we propose a novel CNN-based saliency detection method through dense recurrent connections and residual-based hierarchical feature integration. Inspired by the recent neurobiological finding that abundant recurrent connections exist in the human visual system, we firstly propose a novel dense recurrent CNN module (D-RCNN) to learn informative saliency cues by incorporating dense recurrent connections into sub-layers of convolutional stages. Then we present a residual-based architecture with short connections for deep supervision which hierarchically combines both coarse-level and fine-level feature representations. Our end-to-end method takes raw RGB images as input and directly outputs saliency maps without relying on any time-consuming pre/post-processing techniques. Extensive qualitative and quantitative evaluation results on four widely tested benchmark datasets demonstrate that our method can achieve more accurate saliency detection results solutions with significantly fewer model parameters.