Miracle-dz/UniFAR-FTUs huggingface dataset:
Extracting geographical semantics from online news articles

Several news articles on the web contain geographical locations as significant elements. For the most part, these locations are not available in a format that is machine interpretable. The machine can read in the text of an article, but not derive an understanding of its content. This project aims to find techniques for detecting and extracting locations from the plain text online news articles. The project is limited to articles written in Norwegian, and published in the county of Hordaland. This is done by using methods from design science, for the development and evaluation. A prototype is implemented as a proof-of-concept system using the Clojure programming language. By text analysis, the prototype is able to find mentions of locations in articles. The prototype system have been made available as an open source project and as a Clojure library.
Anonymous Datagrams over DNS Records

Many privacy-enhancing architectures require an anonymous communication channel as a component to acquire tokens or other material from a centralized authority without revealing the requester's identity. While anonymous routing overlays like Tor can provide the required anonymity for TCPlike data streams, it increases dependence on third party infrastructure, and widespread use may overload the Tor network's current capacity. Therefore, we propose to use requests in the Domain Name System as an anonymization layer by forwarding packets over public DNS resolvers operating in recursive mode. In contrast to existing approaches, we focus on UDP datagram transport. As an example use case, we implemented a system for anonymous issuance of blind signatures. We evaluate our domaintailored solution against existing approaches like Tor and analyze its performance and privacy guarantees. Evaluation results show that the achievable data rate using the DNS-based anonymous channel is sufficient for many practical use cases, and that the provided anonymity is comparable to that provided by the Tor network.
Constructive Interference Optimization for Data-Aided Precoding in Multi-User MISO Systems

Unlike the general concept of eliminating or avoiding inter-user interference in the downlink multi-user multiple-input single-output (MISO) system, the data-aided precoding scheme attempts to exploit the constructive interference at symbol level. Positive interference can be constructed to enhance the received signal gain by predicting the phase and magnitude of the inter-user interference. In this paper, we formulate a constructive interference optimization problem that minimizes a sum of minimum mean square error (MMSE) for all users, while ensuring the minimum required constructive interference gain with a fixed total power constraint. As opposed to the existing scheme, such as constructive zero-forcing precoding or minimum power precoding subject to strict phase conservation for phase-shift keying (PSK), our proposed scheme exploits a full range of relaxation for the constructive interference region, while ensuring the link performance by minimizing the sum of mean-square error (MSE) for all users. In fact, the relaxed requirements lead to more degrees of freedom for improving the cumulative constructive interference gain (CCIG) under the varying channel conditions. Furthermore, our MMSE-based optimization approach allows for a semi-closed-form optimal solution to the data-aided precoding scheme, providing a more CCIG without incurring unacceptable complexity than other state-of-the-art schemes.
Design of Optimal PID Control of DC MOTOR Using Genetic Algorithm

Abstract—Wide amplitude, DC motor's speed and their facile control cause its great application in industries. Generally the DC motors gain speed by armature voltage control or field control. In this paper, by using a combination of Genetic Algorithms, we have tried to optimally control the inverted pendulum by nonlinear equations. The results of this simulation have been mentioned in the conclusion. It seems that the results be acceptable results.
ANTSdroid: Using RasMMA Algorithm to Generate Malware Behavior Characteristics of Android Malware Family

Malware developers often use various obfuscation techniques to generate polymorphic and metamorphic versions of malicious programs. As a result, variants of a malware family generally exhibit resembling behavior, and most importantly, they possess certain common essential codes so to achieve the same designed purpose. Meantime, keeping up with new variants and generating signatures for each individual in a timely fashion has been costly and inefficient for anti-virus software companies. It motivates us the idea of no more dancing with variants. In this paper, we aim to find a malware family's main characteristic operations or activities directly related to its intent. We propose a novel automatic dynamic Android profiling system and malware family runtime behavior signature generation method called Runtime API sequence Motif Mining Algorithm (RasMMA) based on the analysis of the sensitive and permission-related execution traces of the threads and processes of a set of variant APKs of a malware family. We show the effectiveness of using the generated family signature to detect new variants using real-world dataset. Moreover, current anti-malware tools usually treat detection models as a black box for classification and offer little explanations on how malwares behave and how they proceed step by step to infiltrate targeted system and achieve the goal. We take malware family DroidKungFu as a case study to illustrate that the generated family signature indeed captures key malicious activities of the family.
Optimality and duality of the turbo decoder : Two optimality criteria which underlie the turbo decoder are reconciled within

The near-optimal performance of the turbo decoder has been a source of intrigue among communications engineers and information theorists, given its ad hoc origins that were seemingly disconnected from optimization theory. Naturally one would inquire whether the favorable perfor- mance might be explained by characterizing the turbo decoder via some optimization criterion or performance index. Recent- ly, two such characterizations have surfaced. One draws from statistical mechanics and aims to minimize the Bethe approx- imation to a free energy measure. The other characterization involves constrained likelihood estimation, a setting perhaps more familiar to communications engineers. The intent of this paper is to assemble a tutorial overview of these recent developments, and more importantly to identify the formal mathematical duality between the two viewpoints. The paper includes tutorial background material on the information geometry tools used in analyzing the turbo decoder, and the analysis accommodates both the parallel concatenation and serial concatenation schemes in a common framework.
Fuzzy Based Optimum Bit Allocation for Rate Control in Video Compression

Abstract This paper presents an algorithm for content-based bit rate allocation for video coding in order to improve rate-distortion (R-D) performance. A fuzzy tool is developed to analyze different areas of a frame. This tool attributes a level of interest to each macroblock. It is effective for a very large range of natural pictures. Beginning with the segmentation of the original picture, a non-hierarchical fuzzy model allows combining the results of distinct criteria in order to increase the reliability of the system and provide convergence to a meaningful final result. R-D optimal rate allocation is done taking help of the fuzzy model and human visual system. The model developed is incorporated in TM5 rate control algorithm and the results are compared with the same.
A Comparative Study of the Approach Provided for Preventing the Data Leakage

Data is the most valuable assets of an organization that need to be secured. Due to limited computational resources, Customers outsource their workload to cloud and economically enjoy the massive computational power, bandwidth, storage, and even appropriate software that can be shared in a pay-per-use manner. Despite tremendous benefits of cloud computing, protection of customers’ confidential data is a major concern. Data leakage involves the intentional or unintentional release of secure or confidential information to the non-trusted environment. Data leakage poses a serious issue for companies as the number of incidents and the cost to those experiencing them continue to increase. Data leakage is enhanced by the fact that transmitted data (both inbound and outbound); including emails, instant messaging, website forms and file transfers. Data leakage prevention system (DLPS) is a strategy for making sure that end users do not send the confidential data or information outside the corporate network. This review paper aims to study data leakage prevention through some challenges and data protection approaches as well as considering some limitations. This survey of DLPS can benefit academics as well as professionals.
Research on Calibration System for Electronic Transformer in Alpine region

The calibration of electronic transfer’s digital out is significant part for the reliable operation of intelligent substation. According to the particular condition in alpine region, a calibration system and a message-analysis sub software which base on IEC61850-9-2 is developed. In order to improve the accuracy and stability of the calibrator, the Hilbert transform, quasi-synchronization and other hardware improvements are introduced for the using environment, which effectively decreases the measurement error caused by non-synchronous sampling and temperature.
Communications software performance prediction

Software development can be costly and it is important that confidence in a software system be established as early as possible in the design process. Where the software supports communication services, it is essential that the resultant system will operate within certain performance constraints (e.g. response time). This paper gives an overview of work in progress on a collaborative project sponsored by BT which aims to offer performance predictions at an early stage in the software design process. The Permabase architecture enables object-oriented software designs to be combined with descriptions of the network configuration and workload as a basis for the input to a simulation model which can predict aspects of the performance of the system. The prototype implementation of the architecture uses a combination of linked design and simulation tools.
Dynamic Light Path Establishment In switch Fabric Using OpenFlow

In today’s optical networks, Light paths are established through Network Management System and Element Management System, which is a manual and cumbersome process. Paths are computed and pre-setup according to the known traffic d emands, a nd p rovisioning a ny new service takes time. Several efforts have recently been made to make the path establishment process dynamic, including the Software Defined N etworking a pproach. H owever all of this work has assumed fully interconnected fabrics of the network devices which is generally not the case. The task of end to end path establishment between network elements and through fabric are interrelated, and this task remains incomplete, without consideration of the switch’s limitations. This work highlights for the first time the issue of path establishment dynamically through a switch fabric. The paper briefly e xplains t he p roblem a nd s uggests an SDN based solution using the OpenFlow protocol. This work contributes a represention of the basic framework which will be required to make the complete path setup process dynamic. The paper also includes an operational description.
Missing Data in Cluster Randomised Trials

Missing outcomes are a commonly occurring problem in cluster randomised trials, which can lead to biased and inefficient inference if ignored or handled inappropriately. Handling missing data in CRTs is complicated due to the hierarchical structure of the data. Two approaches for analysing such trials are cluster-level analysis and individual level analysis. An assumption regarding missing outcomes in CRTs that is sometimes plausible is that missingness depends on baseline covariates, but conditioning on these baseline covariates, not on the outcome itself, which is known as a covariate dependent missingness (CDM) mechanism. The aim of my thesis was to investigate the validity of the approaches to the analysis of CRTs for the three most common outcome types: continuous, binary and time-to-event, when outcomes are missing under the assumption of CDM. Missing outcomes were handled using complete records analysis (CRA) and multilevel multiple imputation (MMI). We investigated analytically, and through simulations, the validity of the different combinations of the analysis model and missing data handling approach for each of the three outcome types. Simulations studies were performed considering scenarios depending on whether the missingness mechanism is the same between the intervention groups and whether the covariate effect is the same between the intervention groups in the outcome model. Based on our analytical and simulations results, we give recommendations for which methods to use when the CDM assumption is thought to be plausible for missing outcomes. The key findings of this thesis are as follows. Continuous outcomes: Cluster-level analyses using CRA are in general biased unless the intervention groups have the same missingness mechanism and the same covariate effects on outcome in the data generating model. In the case of individual-level analysis, the linear mixed model (LMM) using CRA adjusted for covariates such that the CDM assumption holds gives unbiased estimates of intervention effect regardless of whether the missingness mechanism is the same or different between the intervention groups, and whether there is an interaction between intervention and baseline covariates in the data generating model for outcome, provided that such interaction is included in the model when required. There is no gain in terms of bias or efficiency of the estimates using MMI over CRA as long as both approaches use the same functional form of the same set of baseline covariates. Binary outcomes: The adjusted cluster-level estimator for estimating risk ratio (RR) using full data is consistent if the true data generating model is a log link model, the functional form of the baseline covariates is the same between the intervention groups, and the random effects distribution is the same between the intervention groups. Cluster-level analyses using CRA for estimating risk difference (RD) are in general biased. For estimating RR, cluster-level analyses using CRA are valid if the…
Blow up the CPU Chains! OpenCL-assisted Network Protocols

In times of cloud services and IoT, network communication takes heavily part of distributed applications.The underlying network protocol processing causes substantial CPU utilization, although recent kernel-bypass frameworks or specialized server network cards reduce the burden for the CPU. Since hardware acceleration is expensive and still not very common, the leverage of available compute power is a logical step, that leads to GPGPU utilization. The new heterogeneous uniform memory access architecture unites CPU and GPU on one die and therefore features a shared memory excluding the overhead of PCI-E communication. In this paper we present SocketCL, a user-space network protocol stack acceleration. In order to utilize the GPU we ported CPU based Ethernet, IPv4 and UDP network protocols to OpenCL 2.0 kernels leveraging the new hUMA architecture. Resulting from minimal copy processes and optimal utilization of a system’s compute resources, SocketCL achieves substantial acceleration of individual network protocols.
Constrained Consensus of Multiple Autonomous Vehicles with Nonconvex Constraints

In this paper, we investigate the consensus problem for multiple autonomous vehicles with nonconvex constraints. We propose a distributed control algorithm and prove that consensus can be reached if the communication network jointly has a spanning tree. The analysis approach is based on the model transformation and the properties of the Metzler matrix and the stochastic matrix.
Allocation Method of an Unknown Search Keyword to a Thesaurus Node by Using Web

日常的な会話の中では, 新語や固有名詞などシソーラスに定義されていない単語 (未知語) が使用される. 未知語についての知識がなければ, 適切に会話を行うことができない. Webを利用することで, 未知語について調べることができる. しかし, Webには膨大な情報が存在するため, 必要な情報を効率的に得ることは困難である. 未知語に対する適切なシソーラスのノードを提示することによって, 未知語の意味を獲得することができる. 未知語理解はコーパスなど言語データに依存する研究が多く, 対応できない未知語が存在するという問題点がある. 本論文では, 連想メカニズムを構成する概念ベースと関連度計算, さらにWebを用いて, 未知語を概念化することで各ノードとの関連性を評価し, 固有名詞を含んだ未知語をシソーラス上の最適なノードへ分類する手法を提案する.
Dynamic Count-Min Sketch for Analytical Queries Over Continuous Data Streams

The methods of approximate query processing have been proposed for analytics over high-speed data streams, which compact continuous streams into a space-constrained sketch and provide reliable estimates for different queries. Count-Min (CM) is the state-of-the-art sketching structure supporting many queries with error-guaranteed estimates under limited space. However, we need to create a counter table beforehand in CM according to the size of data streams, while it is usually unpredictable for dynamic data streams. In this paper, we proposed an approach, called Dynamic Count-Min sketch (DCM), which is appropriate for dynamic data set and can provide accurate estimates for point query and self-join size query. Our approach constitutes incremental CM sketches and allocates space in a pay-as-you-go manner. Our mathematical analysis and substantial experiments both show that our approach is appropriate for data sets with dynamic or skewed inputs and can provide error-guaranteed estimates with less space compared to CM.
Unsupervised detection of cell-assembly sequences with edit similarity score

Cell assembly is a hypothetical functional unit of information processing in the brain. While technologies for recording large-scale neural activity have been advanced, mathematical methods to analyze sequential activity patterns of cell-assembly are severely limited. Here, we propose a method to extract cell-assembly sequences repeated at multiple time scales and various precisions from irregular neural population activity. The key technology is to combine “edit similarity” in computer science with machine-learning clustering algorithms, where the former defines a “distance” between two strings as the minimal number of operations required to transform one string to the other. Our method requires no external references for pattern detection, and is tolerant of spike timing jitters and length irregularity in assembly sequences. These virtues enabled simultaneous automatic detections of hippocampal place-cell sequences during locomotion and their time-compressed replays during resting states. Furthermore, our method revealed previously undetected cell-assembly structure in the rat prefrontal cortex during goal-directed behavior. Thus, our method expands the horizon of cell-assembly analysis.
Leveraging Sequence-to-Sequence Speech Synthesis for Enhancing Acoustic-to-Word Speech Recognition

Encoder-decoder models for acoustic-to-word (A2W) automatic speech recognition (ASR) are attractive for their simplicity of architecture and run-time latency while achieving state-of-the-art performances. However, word-based models commonly suffer from the-of-vocabulary (OOV) word problem. They also cannot leverage text data to improve their language modeling capability. Recently, sequence-to-sequence neural speech synthesis models trainable from corpora have been developed and shown to achieve naturalness com- parable to recorded human speech. In this paper, we explore how we can leverage the current speech synthesis technology to tailor the ASR system for a target domain by preparing only a relevant text corpus. From a set of target domain texts, we generate speech features using a sequence-to-sequence speech synthesizer. These artificial speech features together with real speech features from conventional speech corpora are used to train an attention-based A2W model. Experimental results show that the proposed approach improves the word accuracy significantly compared to the baseline trained only with the real speech, although synthetic part of the training data comes only from a single female speaker voice.
The Propagation of Counteracting Information in Online Social Networks: A Case Study

Information propagation in online social networks has drawn a lot of attention from researchers in different fields. While prior works have studied the impact and speed of different information propagation in various networks, we focus on the potential interactions of two hypothetically opposite pieces of information, negative and positive. We experiment the amount of time that is allowed for the positive information to be distributed with wide enough impact after the negative information and different selection strategies for positive source nodes. Our results enable the selection of a set of users based on a limited operating budget to start the spread of positive information as a measure to counteract the spread of negative information. Among different methods, we identify that both eigenvector and betweenness centrality are effective selection metrics. Furthermore, we quantitatively demonstrate that choosing a larger set of nodes for the spread of positive information allows for a wider window of time to respond in order to limit the propagation of negative information to a certain threshold.
UA-DETRAC 2018: Report of AVSS2018 & IWT4S Challenge on Advanced Traffic Monitoring

A desirable smart traffic-monitoring and street-safety system can elicit and support the intervention of law enforcement agencies or medical staff. Recently, there has been a dramatically higher demand for such smart systems. To this end, the International Workshop on Traffic and Street Surveillance for Safety and Security (IWT4S) was organized in conjunction with the 15th IEEE International Conference on Advanced Video and Signal-based Surveillance (AVSS 2018). Our goal is to advance the state-of-the-art detection and tracking algorithms and provide a comprehensive performance evaluation for them. We evaluate 5 submitted detection and 7 submitted tracking methods on the large-scale UA-DETRAC benchmark, and the results are shared publicly on the website http://detrac-db. rit.albany. edu. We expect this challenge to advance the research and development of new detection and tracking methods for transportation applications.
Exploring awareness related usability problems of collaborative software with a team usability testing approach

Since virtual teamwork became usual in everyday work, the need for collaborative software is growing. Collaborative software requires special usability testing techniques because of the team level usability problems, which cannot be measured with individual methods. Team usability testing approach makes an attempt to explore team level problems. The theoretical background of this method includes the mechanics of collaboration, which are basic actions which group members need to be able to do in order to use a shared workspace effectively. Team usability testing is an empirical usability evaluation method built on the mechanics of collaboration to explore team level usability problems of collaborative software. The method involves communication analysis, behavior analysis and analysis of post-experiment interviews. This method was applied in a laboratory experiment with 7 teams, 3 members per each. Team members had to collaborate and solve a common task using the same software. The main aim of this article is to introduce the details of team usability testing method. Besides, early group interview results of an experiment based on the real-life usage of this new method and the plan of the future analysis of the data will be discussed.