Reviewerly/exHarmony huggingface dataset:
Universal Approximation with Certified Networks

Training neural networks to be certifiably robust is critical to ensure their safety against adversarial attacks. However, it is currently very difficult to train a neural network that is both accurate and certifiably robust. In this work we take a step towards addressing this challenge. We prove that for every continuous function $f$, there exists a network $n$ such that: (i) $n$ approximates $f$ arbitrarily close, and (ii) simple interval bound propagation of a region $B$ through $n$ yields a result that is arbitrarily close to the optimal output of $f$ on $B$. Our result can be seen as a Universal Approximation Theorem for interval-certified ReLU networks. To the best of our knowledge, this is the first work to prove the existence of accurate, interval-certified networks.
Technology integration in education: Effectiveness, pedagogical use and competence

This cross-sectional study examined primary and secondary teachers’ and students’ views regarding technology integration into teaching and learning in Muscat, Oman. The target group consisted of teachers (N=44) and students (N=219) from two bilingual and two international schools in Oman. Results showed that the majority of students use technology to do projects and research. Both teachers and students enjoy the use of technology in teaching and learning, they feel competent in using it and they similarly evaluate the effectiveness of available technologies. Although the majority of teachers agreed that teaching has become easier and faster with technology, only a small percentage integrates technology into teaching. Factors hindering technology integration concern curriculum design, network issues, time constraint and other. In theory, the study contributes to our understanding of the reasons why teachers’ and students’ find it challenging to use technological innovations in school. Methodologically, this study provides the basis for examining Technological Pedagogical Knowledge in certain socio-geographical contexts. In practice, the study provides educational authorities with suggestions on how technology can be used to support, enhance and extend the curriculum.
Deep Learning for RF Signal Classification in Unknown and Dynamic Spectrum Environments

Dynamic spectrum access (DSA) benefits from detection and classification of interference sources including in-network users, out-network users, and jammers that may all coexist in a wireless network. We present a deep learning based signal (modulation) classification solution in a realistic wireless network setting, where 1) signal types may change over time; 2) some signal types may be unknown for which there is no training data; 3) signals may be spoofed such as the smart jammers replaying other signal types; and 4) different signal types may be superimposed due to the interference from concurrent transmissions. For case 1, we apply continual learning and train a Convolutional Neural Network (CNN) using an Elastic Weight Consolidation (EWC) based loss. For case 2, we detect unknown signals via outlier detection applied to the outputs of convolutional layers using Minimum Covariance Determinant (MCD) and k-means clustering methods. For case 3, we extend the CNN structure to capture phase shifts due to radio hardware effects to identify the spoofing signal sources. For case 4, we apply blind source separation using Independent Component Analysis (ICA) to separate interfering signals. We utilize the signal classification results in a distributed scheduling protocol, where in-network (secondary) users employ signal classification scores to make channel access decisions and share the spectrum with each other while avoiding interference with out-network (primary) users and jammers. Compared with benchmark TDMA-based schemes, we show that distributed scheduling constructed upon signal classification results provides major improvements to in-network user throughput and out-network user success ratio.
Pattern and frontier-based, efficient and effective exploration of autonomous mobile robots in unknown environments

This paper proposes a method with which mobile robots can explore unknown environments efficiently and effectively. For mobile robots, developing exploration methods in unknown environments is a challenging target. Navigation for environment monitoring is necessary in order to explore efficiently in unknown environment. Although a frontier-based exploration can be seen as a conventional method, this method is not necessarily a global optimal planning method, because it explores for the local optimal in each situation; in the monitoring task, it could fall into the local solution, because it could not take into consideration the overall observation efficiency. The proposed method exploits the whole observation and knowledge about the area of the unknown environment. It is possible for the proposed method to establish a global plan in consideration of efficiency and effectiveness by integrating pattern and frontier-based exploration. In simulation, it is shown that the proposed method of pattern and frontier-based exploration is useful for exploring unknown environments efficiently and effectively.
Improving Noise Robustness In Speaker Identification Using A Two-Stage Attention Model

While the use of deep neural networks has significantly boosted speaker recognition performance, it is still challenging to separate speakers in poor acoustic environments. To improve robustness of speaker recognition system performance in noise, a novel two-stage attention mechanism which can be used in existing architectures such as Time Delay Neural Networks (TDNNs) and Convolutional Neural Networks (CNNs) is proposed. Noise is known to often mask important information in both time and frequency domain. The proposed mechanism allows the models to concentrate on reliable time/frequency components of the signal. The proposed approach is evaluated using the Voxceleb1 dataset, which aims at assessment of speaker recognition in real world situations. In addition three types of noise at different signal-noise-ratios (SNRs) were added for this work. The proposed mechanism is compared with three strong baselines: X-vectors, Attentive X-vector, and Resnet-34. Results on both identification and verification tasks show that the two-stage attention mechanism consistently improves upon these for all noise conditions.
A statistical approach to the design of privacy-preserving services

Electronic services have become an indispensable part of society. Billions of users rely on these services every day to communicate with friends, meet new people, buy products, and keep track of their activities. Electronic services provide many comforts to society, but also pose new threats to the privacy of their users. This is due to the fact that users of electronic services send their sensitive information over a communication channel (typically, the Internet), and this information can many times be observed by an unwanted party. Even though encryption can protect the content of communications against unwanted observers, there are other privacy problems that encryption does not solve. In this thesis, we study two of these problems. First, we tackle the problem of meta-data leakage against a passive eavesdropper. Meta-data is information related to a communication other than the content of the communication itself, such as who the communicating parties are, how often they communicate, or where they are located. Meta-data is usually sensitive, so it is important that users hide it from eavesdroppers. In the first part of this thesis, we study a particular solution to meta-data leakage: mix-based anonymous communication systems. We analyze these systems, and find out how to optimally configure their parameters so as to maximize the users' privacy. In the second part of the thesis, we study how to protect users against an adversarial service provider. We consider the particular case of Location-Based Services (LBS), where users want to obtain some service that depends on their real location (e.g., finding nearby points of interest), but do not want to share this location with the service provider. We study obfuscation-based location privacy mechanisms, that allow users to obtain some utility from the LBS without revealing their actual location. We find weaknesses in the approach that previous works follow to design and evaluate location privacy-preserving mechanisms, and propose solutions to mend these issues and improve current designs. Throughout the thesis we follow a statistical approach to improve the privacy of electronic services: we model both the system operation and the users' behavior, and leverage these models to optimize the privacy of the systems. This approach provides theoretical guarantees that our results will be universally valid as long as the models that we assume for user behavior hold. Also, our methodology can be easily adapted to other privacy problems, and we hope it will inspire future research in this direction.
Mining New Scientific Research Ideas from Quantum Computers and Quantum Communications

This paper studies the significant scientific ideas presented in quantum computers and quantum communications. In quantum computers, a quantum has no precise position. It does not follow the causal law and does not hold repeatability. Quantum addition takes almost no time. A quantum (qubit) can present both “1” and “0” at the same time. In quantum communications, when two particles are entangled, one can transfer information to the other that can even across cosmic distance away without energy, time, and any ”field” as medium. All these ideas together form a set of completely new ideas of “rational science”, breaking through the concepts of classical science. However, the slow research progress of quantum computers and quantum communications reflects that people’s knowledge of scientific ideas is very limited. This paper is to discuss the evolution of scientific ideas inspired by quantum computers and quantum communications for further mining the scientific research ideas.
Historical Relation Extraction from Buddhist Temple Documents of the Lanna Kingdom

A substantial amount of valuable knowledge has been recorded, namely historical documents, in the form of unstructured data.The Extraction of such information will provide useful historical knowledge.The objective of this study, is to propose a relation extraction technique of unstructured data from Buddhist temple documents in the Lanna Kingdom, consisting of relationships including the name of the kings who built the temple, the name of the king who created or restored a historical site within its grounds and the Buddhist era the temple was built.The research process began with collecting temple data located in the upper northern part of Thailand, with published documentation of temple history in electronic form from Temples Thailand online website.The data was transformed into an electronic document or a text file, then passed to the LEXTO program for word segmentation, while retaining the original meaning.We designed and developed an algorithm to extract relationships between named entities from Buddhist temple documents, and the application of information extraction principles to the process.We then evaluated the 1,093 original documents by using the algorithm to extract the required information.From the research, the results indicated that the proposed technique can be applied effectively, to accurately extract the relations related to Lanna kingdom history from Buddhist temple documents.
Differentially Private Controller Synthesis With Metric Temporal Logic Specifications

Privacy is an important concern in various multiagent systems in which data collected from the agents are sensitive. We propose a differentially private controller synthesis approach for multi-agent systems subject to high-level specifications expressed in metric temporal logic (MTL). We consider a setting where each agent sends data to a cloud (computing station) through a set of local hubs and the cloud is responsible for computing the control inputs of the agents. Specifically, each agent adds privacy noise (e.g., Gaussian noise) point-wise in time to its own outputs before sharing them with a local hub. Each local hub runs a Kalman filter to estimate the state of the corresponding agent and periodically sends such state estimates to the cloud. The cloud computes the optimal inputs for each agent subject to an MTL specification. While guaranteeing differential privacy of each agent, the controller is also synthesized to ensure a probabilistic guarantee for satisfying the MTL specification.We provide an implementation of the proposed method on a simulation case study with two Baxter-On-Wheels robots as the agents.
Institutional repositories and the item and research data metrics landscape

The success of COUNTER in supporting adoption of a standard to measure e-resource usage over the past 15 years is apparent within the scholarly communications community. The prevalence of global OA policies and mandates, and the role of institutional repositories within this context, prompts demand for more granular metrics. It also raises the profile of data sharing of item-level usage and research data metrics. The need for reliable and authoritative measures is paramount. This burgeoning interest is complemented by a number of initiatives to explore the measurement and tracking of usage of a broad range of objects outside traditional publisher platforms. Drawing on examples such as OpenAIRE, IRUS-UK, Crossref’s Distributed Usage Logging and Event Data service and COAR Next Generation Repositories, this article provides a brief introduction and overview of developments in this area.This paper forms the basis of a session that was originally presented at the UKSG Annual Conference in Telford in April 2019.
A Hierarchical Approach for Visual Storytelling Using Image Description

One of the primary challenges of visual storytelling is developing techniques that can maintain the context of the story over long event sequences to generate human-like stories. In this paper, we propose a hierarchical deep learning architecture based on encoder-decoder networks to address this problem. To better help our network maintain this context while also generating long and diverse sentences, we incorporate natural language image descriptions along with the images themselves to generate each story sentence. We evaluate our system on the Visual Storytelling (VIST) dataset and show that our method outperforms state-of-the-art techniques on a suite of different automatic evaluation metrics. The empirical results from this evaluation demonstrate the necessities of different components of our proposed architecture and shows the effectiveness of the architecture for visual storytelling.
WiderPerson: A Diverse Dataset for Dense Pedestrian Detection in the Wild

Pedestrian detection has achieved significant progress with the availability of existing benchmark datasets. However, there is a gap in the diversity and density between real world requirements and current pedestrian detection benchmarks: 1) most of existing datasets are taken from a vehicle driving through the regular traffic scenario, usually leading to insufficient diversity; 2) crowd scenarios with highly occluded pedestrians are still under represented, resulting in low density. To narrow this gap and facilitate future pedestrian detection research, we introduce a large and diverse dataset named WiderPerson for dense pedestrian detection in the wild. This dataset involves five types of annotations in a wide range of scenarios, no longer limited to the traffic scenario. There are a total of $13,382$ images with $399,786$ annotations, i.e., $29.87$ annotations per image, which means this dataset contains dense pedestrians with various kinds of occlusions. Hence, pedestrians in the proposed dataset are extremely challenging due to large variations in the scenario and occlusion, which is suitable to evaluate pedestrian detectors in the wild. We introduce an improved Faster R-CNN and the vanilla RetinaNet to serve as baselines for the new pedestrian detection benchmark. Several experiments are conducted on previous datasets including Caltech-USA and CityPersons to analyze the generalization capabilities of the proposed dataset and we achieve state-of-the-art performances on these previous datasets without bells and whistles. Finally, we analyze common failure cases and find the classification ability of pedestrian detector needs to be improved to reduce false alarm and miss detection rates. The proposed dataset is available at http://www.cbsr.ia.ac.cn/users/sfzhang/WiderPerson
An information theoretic model for summarization, and some basic results

A basic information theoretic model for summarization is formulated. Here summarization is considered as the process of taking a report of v binary objects, and producing from it a j element subset that captures most of the important features of the original report, with importance being defined via an arbitrary set function endemic to the model. The loss of information is measured by a weight average of variational distances, which we term the semantic loss.Our results include both cases where the probability distribution generating the v-length reports are known and unknown. In the case where the generating distribution is known, our results demonstrate how to construct minimal semantic loss summarizers. For the case where the probability distribution is unknown, we show how to construct summarizers which minimize the semantic loss averaged uniformly over all possible distribution converges to the minimum.
An Evaluation of Game Usability in Shared Mixed and Virtual Environments

Augmented reality (AR) and virtual reality (VR) technologies are increasingly becoming more pervasive and important in the information technology area.Thanks to the technological improvements, desktop interfaces are being replaced by immersive VR devices that offer a more compelling game experience.AR games have started to draw attention of researchers and companies for their ability to exploit both the real and virtual environments.New fascinating challenges are generated by the possibility of designing hybrid games that allow several users to access shared environments exploiting the features of both AR and VR devices.However, the user experience and usability can be affected by several parameters, such as the field of view (FoV) of the employed devices or the realism of the scene.The work presented in this chapter aims to assess the impact of the FoV on the usability of the interfaces in a first-person shooter game.Two players, interacting with AR (first player) and VR (second player) devices, can fight each other in a large game environment.Although we cannot ascertain that different FoVs have affected the game usability, users considered the narrow FoV interfaces to be less usable, even though they could freely move around the real environment.
A Self-Play Policy Optimization Approach to Battling Pokémon

Pokémon is a popular role-playing video game franchise with a long-lived competitive scene that has evolved throughout the last two decades. The game exhibits several properties that come together to present a worthy challenge for AI agents to tackle. In this work, we present a low-cost self-play based reinforcement learning approach to the competitive battling aspect of the game. The proposed agent was tested and trained in a variety of environments designed to simulate possible use cases of such an AI. Experiments demonstrate that the agent is capable of performing on par with the state of the art in search-based Pokémon AI, as well as being competitive with́ human players on a popular matchmaking ladder. Furthermore, we investigate the transferability of trained skill-whether an agent trained in one environment performs well in a different environment.
Additional lossless compression of JPEG images based on BPG

The task of additional lossless compression of JPEG images is considered. We propose to decode JPEG image and recompress it using lossy BPG (Better Portable Graphics) codec based on a subset of the HEVC open video compression standard. Then the decompressed and smoothed BPG image is used for calculation and quantization of DCT coefficients in 8x8 image blocks using quantization tables of the source JPEG image. A difference between obtained quantized DCT coefficients and quantized DCT coefficients of the source JPEG image (prediction error) is calculated. The difference is lossless compressed by a proposed context modeling and arithmetical coding. In this way the source JPEG image is replaced by two files: compressed BPG image and the compressed difference which needed for lossless restoration of the source JPEG image. It is shown that the proposed approach provides compression ratios comparable with state of the art PAQ8, WinZip and STUFFIT file archivers. At the same time BPG images may be used for fast preview of compressed JPEG images.
User Authentication Based on Knowledge of Their Work on the Internet

This chapter analyzes existing user authentication methods for remote access to information systems and disadvantages of these methods. The method of multifactor authentication of users when they are accessing remote information systems, combining validation of knowledge on secret password and verification of conformity of the habits and preferences of Internet user’s interests, is defined by registration in the system. Using the history of Web pages, the Internet browser creates a list of Web pages the user has visited in the past period of time. It is proposed to use the Bayesian classification for user’s knowledge based on the analysis of information about Web pages visited by the user. For user authorization from someone else’s computer, the user is invited to ask for additional questions to test knowledge of subject areas, which they selected during registration in the information system. This chapter defines the language and tools for implementation of the proposed authentication algorithm: the programming language PHP and the MySQL database management system (to create a database of registered users), Web-based open source application phpMyAdmin (to create and administer MySQL database management system), and the JavaScript programming language and HTML (for creating extensions for browsers receiving a list of the addresses of the Web pages visited by the user).
The Effects of Macroeconomic Indicators on Leveraged Forex Volume: Evidence from Turkey

In this study, the effects of macroeconomic indicators on leveraged forex volumes of financial intermediary institutions have been analyzed. The study covers the data for the period 2013Q1-2017Q2 of 16 financial intermediary institutions operating in Turkey. In the study in regards to macroeconomic indicators, BIST 100 Index revenues (BIST), consumer price index (CPI), consumer confidence index (CCI), average rate applied to deposits opened by banks (INT), gold prices (GOLD), money supply (M2), external trade export unit index (EUVI) and unemployment rate (UNEMP) have been used. In the study, the effects of macroeconomic indicators on leveraged forex volume have been examined through panel data analysis. As a result of the panel data analysis, it has been determined that the BIST, M2, EUVI and UNEMP variables have positive significant effects on leveraged forex volume and that the INT variable has negative significant effects on leveraged forex volume. In the study, the effects of CPI, CCI and GOLD variables on leveraged forex volume are statistically insignificant.