Miracle-dz/UniFAR-FTUs huggingface dataset:
On Optimizing DNA Sequence Design for DNA Logic AND Circuit

DNA logic circuit is a popular research topic in biological computing. Initially, simple DNA logic gates were introduced. Later on, models of DNA logic circuits were proposed and simple circuits were successfully demonstrated. This paves way for the invention of DNA computers. However, constructing efficient logic gates from DNA materials is a very complex task because of the properties of DNA, which consists only of 4 different types of nucleotides and the chemical bonding possibilities between different pairs of DNA nucleotides. Constructing logic gates would yield unwanted secondary structures of logic gates because there are a lot of possibilities for other DNA structures to be created from using only four types of nucleotides due to different bonding alignment patterns.This research aims to develop a method which selects the most suitable DNA sequence to be used to construct DNA logic AND gate. This work is a proof of concept that DNA logic gate could be designed more efficiently. Our method is to generate all possible sequences (considering all alignment positions) and select the sequences that yield highest possibility of constructing such a structure. The selected sequences were then applied with the primer design rules in order to filter undesired sequences according to the biological requirements. We performed experiments in silico and the experimental results showed that 75% of the outputs from the application could form our expected structure with highest probability. This significantly helps us save cost from running experiments in vitro.
Improving Word Representation with Word Pair Distributional Asymmetry

Distributed word representation has demonstrated impressive improvements on numerous natural language processing applications. However, most existing word representation learning methods rarely consider use of word order information, and lead to confusion of similarity and relevance. Targeting on this problem we propose a general learning approach DAV (Distributional Asymmetry Vector) to build better word representation by utilizing word pair distributional asymmetry, which contains word order information. Experimental study on two large benchmarks with several state-of-art word representation learning models has shown the potential of the proposed method.
Automated peak picking using region‐based convolutional neural network for operational modal analysis

Peak picking is used to determine locations of salient peaks in a graphical representation of a physical quantity. It is often used to extract possible natural frequencies from the frequency domain representation of structural responses. One of the challenges in peak picking is to establish a method to automatically distinguish peaks from data containing noise peaks. As selecting peaks intrinsically depends on human perception, algorithms for automated peak picking have exhibited only partial success to date. This paper presents an automated peak picking method that utilizes a region‐based convolutional neural network with possible object locations to accurately identify the peaks. A tailored deep learning architecture is proposed, which is subsequently trained using only the peaks numerically generated by computer software. Laboratory and field tests are conducted using acceleration responses obtained from three test structures of beam, truss, and stay cable to verify the identification performance of the proposed peak detector. The proposed peak detector is shown to successfully identify most of the salient peaks with high accuracy.
BMK Stick: IMU-Based Motion Recognition Air Mouse and Five-Multikey Keyboard

BMK stick, a Bluetooth mouse with a keyboard is a joystick-type input device based on an inertial measurement unit (IMU) sensor that recognizes human hand movements along with the combinations of five buttons and enables users to input text with one hand. It is designed as an input device for computers and virtual environments. The BMK stick has two modes, mouse mode (mode 0) and keyboard mode (mode 1), which can be set by pushing a button. In mode 0, the IMU sensor extracts the value of the user’s hand motion with the 3D Euler angle. The extracted values are then converted into two-dimensional Cartesian coordinates through a built-in algorithm in the microcontroller unit (MCU). The converted value is mapped onto the computer screen to control the mouse pointer. In mode 1, text can be entered with with one hand using a five-button combination by using an AEIOU key-map provided by a commercial text-entry device, TAP. Two experiments were conducted to evaluate the performance of the BMK stick. In the first experiment, the mouse pointing performance was compared with three existing commercial ones. In the second experiment, the text-entry rate was evaluated in words per minute (WPM) compared with the two existing commercial one-handed keyboard input devices. The result shows that our proposed device in the first experiment performed better than the other commercial products. In the second experiment, even though some experimenters had some difficulties in manipulating our device, it produced more WPM than the commercial ones.
Emotion Detection from Speech Signals using Voting Mechanism on Classified Frames

Understanding human emotion is a complicated task for humans themselves, however, this did not stop the researchers from trying to make machines capable of understanding human emotions. Many approaches have been followed, using speech signals to detect emotions has been popular among these approaches. In this study, Mel Frequency Cepstrum Coefficient (MFCC) features were extracted from speech signals to detect the underlying emotion of the speech. Extracted features were used to classify different emotions using LMT classifier. For each frame of a speech signal, 13-dimensional feature vectors were extracted and Logistic Model Tree (LMT) models were trained using these features. For classifying an unknown speech signal, the 13-dimensional frame features are first extracted from the signal and each frame is classified using the trained model. Using a voting mechanism on the classified frames, the emotion of the speech signal is detected. Experimental results on two datasets- Berlin Database of Emotional Speech (Emo-DB) and Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) show that our approach works very well in classifying certain emotions while it struggles to discern the differences between some pairs of emotions. Among the trained models, the maximum accuracy achieved was 70% in detecting 7 different emotions. Considering the small dimension size of the feature vectors used, this approach provides an efficient solution to classifying different emotions using speech signals.
Preserving Privacy for Hubs and Links in Social Networks

Online social networks provide valuable information for researchers. However, users' privacy will be exposed when the social network data is published. Social relationships are connected with our social identification which represent users' sensitive message. In addition, there are some nodes in the social network that are closely connected to others, called hub nodes. These hub nodes play an important role in the social network. Many proposed methods only protect the privacy of social relationships, the hub nodes with a large amount of neighbors are exposed to the adversary. Therefore the adversary can easily find out the identity of these nodes, and then inferring the probability of the existence of a link, although the link is perturbed. To tackle this problem, we proposed a random walk based algorithm which can protect both hub nodes and social relationships. Through a random degree perturbation algorithm, the performance of link privacy preserving method can be improved. We conducted comprehensive experiments over the real-world social network dataset to evaluate the anonymity level and utility of the transformed graph. The experimental results show that the proposed mechanism can achieve higher link privacy and has better utility, outperforming the state of the art approaches.
Is GPUs Ready to Boost Genomic Alignment Computation

This paper presents a comparative study of CPU and GPU performance in sequence alignments for the Next Generation Sequencing (NGS), specifically in the context of short reads mapping to a reference genome. Bowtie2 and BWA which runs on CPU only and its equivalent, NvBowtie and BarraCUDA which runs on both CPU and GPU, is chosen as the genomic tools benchmark for this studies. The CPUs selected for our study is Intel Xeon Processor E5-2695v2(x2) and Intel Xeon Processor E5-2699 v4(x2). The GPUs that we have selected for our study are the NVIDIA Tesla K80, Pascal 100 (P100) and Volta 100 (V100). Our results show that GPUs performs better than CPUs for long read’s length and large number of reads. However, taking the price/performance ratio into account, the results suggests a case of diminishing return for the newer generation of NVIDIA GPU.
Materials Informatics for Process and Material Co-optimization

In semiconductor manufacturing, fabrication processes and their materials should be properly co-optimized to achieve required processing results. Unfortunately, it is a very time-consuming procedure, because the number of possible combinations of process/material candidates is very large. Here, we develop a methodology for co-optimization of processes and their materials. We successfully constructed a prediction model for dry-etching of high-k materials (R2)>0.65). By trying only <0.00001% of all possible process/material candidates with this model and Bayesian optimization, we can find new combinations of gasses and their processes for 15-20 times higher etching rates than that with a traditional gas/process condition.
Participating-Domain Segmentation Based Delay-Sensitive Distributed Server Selection Scheme

This paper proposes a participating-domain segmentation based server selection scheme in a delay-sensitive distributed communication approach to reducing the computational time for solving the server selection problem. The proposed scheme divides the users’ participation domain into a number of regions. The delay between a region and a server is a function of locations of the region and the server. The length between the region and the server is considered based on conservative approximation. The location of the region is determined regardless of the number of users and their participation location. The proposed scheme includes two phases. The first phase uses the server finding process and determines the number of users that are accommodated from each region by each server, instead of actual server selection, to reduce the computational complexity. The second phase uses the delay improvement process and determines the overall delay and the selected server for each user. We formulate an integer linear programming problem for the server selection in the proposed scheme and evaluate the performance in terms of computation time and delay. The numerical results indicate that the computational time using the proposed scheme is smaller than that of the conventional scheme, and the effectiveness of the proposed scheme enhances as the number of users increases.
Measuring Architectural Stability and Instability in the Evolution of Software Systems

When a system is unstable, its output may be infinite even though the input to the system is finite, (i.e. producing unbounded output for a bounded input). The consequences of unstable software can lead to a system that is unpredictable, unsafe and unreliable, leading to a system that is highly likely to fail in delivering its required functionalities. Understanding the pattern of software architectural change as the software evolves over time is vital in making sure that systems retain their stability and can deliver their required functionalities as they evolve. Studying structural change in software systems can help identify hidden features that can be used as a guidance for subsequent software evolutions. Measuring the stability or instability in ob-j ect-oriented software systems in this research is based on the premise that if the domain abstraction of a software are stable, then the software structural components are highly likely to remain the same throughout the evolution of the software system. This research carefully profiled selected metrics from evolution repository datasets for large-scale open source software systems. Metric value fluctuations were studied, from which new reliable and empirically derived metrics are introduced. A process for monitoring and evaluation of stability of evolving e-type software systems is also presented. The set of metrics introduced will act as a numerical guideline for decision makers and developers to use in developing future versions of an evolving Software systems
Wideband Reconfigurable Antenna With Simple Biasing Circuit and Tri-Polarization Diversity

This letter presents a wideband reconfigurable antenna with tri-polarization diversity. The proposed antenna has several exclusive features including: wide operating bandwidth (BW); removal of biasing wires to avoid their effects on the antenna's RF performance; and simple reconfigurability using only two p-i-n diodes and one switchable voltage. A microstrip patch is chosen as a primary polarization-reconfigurable source of the designed antenna. Next, a metasurface (MS) is employed to accomplish a significantly increased circularly polarized (CP) BW and the realization of linearly polarized (LP) operation. An antenna prototype is fabricated and experimentally tested to authenticate the simulated results. The measurements confirm that the proposed design exhibits wide overlapped BW of 12.4% and broadside gain of better than 5.8 dBi for dual-sense CP and LP states.
SYMBOL-ASYNCHRONOUS TRANSMISSION IN MULTIBEAM SATELLITE USER DOWN-LINK: RATE REGIONS FOR NOVEL SUPERPOSITION CODING SCHEMES

We consider the forward link of a multibeam satellite system with high spectral reuse and the novel low-complexity transmission and detection strategies from [1]. More specifically, we study the impact of a time offset between the antenna beams that cooperate to simultaneously serve a given user. Assuming Gaussian signaling, we provide closed-form expressions for the achievable rate region. It is demonstrated that, in the absence of timing information at the gateway, this region is not affected by a time offset. Our numerical results further show that, in case timing is known at the gateway, an offset of half a symbol period at both user terminals is optimal in terms of spectral efficiency.
Security Vulnerabilities and Countermeasures in the RPL-Based Internet of Things

RPL is a routing protocol for Low-Power and Lossy Networks, which is the routing protocol standardized for the Internet of Things (IoT). However, RPL has many serious vulnerabilities which can be exploited by attackers. In this paper, we adopt an finite state machines(FSM) based vulnerability discovering approach to analyze the security vulnerabilities of RPL, which can successfully discover sinkhole attack, selective-forwarding attack and hello flood attack. Then we propose some security countermeasures to defend against the attacks. Finally, we run a thorough set of simulations to assess the impact of the sinkhole attack and the effectiveness of proposed countermeasure. The results show that the attack can significantly damage the RPL and the countermeasure can successfully detect the malicious node.
Identifying Abusive Comments in Hebrew Facebook

In this study, we aim to classify comments as abusive or non-abusive. We develop a Hebrew corpus of user comments annotated for abusive language. Then, we investigate highly sparse n-grams representations as well as denser character n-grams representations for comment abuse classification. Since the comments in social media are usually short, we also investigate four dimension reduction methods, which produce word vectors that collapse similar words into groups. We show that the character n-grams representations outperform all the other representation for the task of identifying abusive comments.
Light Sensor Based Occupancy Estimation via Bayes Filter With Neural Networks

Building occupancy estimation holds great promise for building control systems to save energy and provide a comfortable indoor environment. Existing solutions turn out to be lacking in practice due to their specific hardware requirements and/or poor performances. Recently, an light-emitting diode (LED) light sensor based occupancy estimation system, which is nonintrusive and does not require any additional hardware, has been proposed. However, the performance of the system is limited, especially in a complicated dynamic scenario. In this article, a Bayes filter with neural networks is proposed for the optimal estimation of occupancy based on light sensor data. Specifically, based on the formulation of Bayes filter, the posterior probability of the building occupancy can be decoupled into three components: The prior, likelihood, and evidence. The prior and likelihood are, respectively, estimated from a Markov model and an efficient single-hidden layer feedforward neural network (SLFN). Finally, the evidence can be obtained by the results of prior and likelihood. Real experiments have been conducted to verify the effectiveness of the proposed approach in two complicated scenarios, i.e., dynamic and regular. Results indicate that the proposed Bayes filter outperforms all the benchmark approaches. The impacts of the number of LED sensing units and the number of hidden layers for neural networks are also evaluated. The results manifest that the number of sensing units should be chosen based on the required performance and the SLFN is sufficient for this application.
A Graph Based Metric To Calculate Significance of Ontological Concepts

Semantic similarity is a special case of semantic relatedness. There are many methods available to design semantic similarity measures based on domain ontology. In this paper, a method to assess the significance of concepts in domain ontology is proposed. The significance value shows the worth of the concept, which can have major contribution in integration, knowledge extraction and many other knowledge engineering fields. The study based on concepts significance in ontology, models concepts' usefulness. We introduced a method for measuring semantic relatedness by knowing the density of each concept by calculating its significance value. In addition, our approach considers a very important property of graph to calculate the degree (in-degree & out-degree). The case study is provided to demonstrate the usefulness of our work.
Characteristics of Co-Allocated Online Services and Batch Jobs in Internet Data Centers: A Case Study From Alibaba Cloud

In order to reduce power and energy costs, giant cloud providers now mix online and batch jobs on the same cluster. Although the co-allocation of such jobs improves machine utilization, it challenges the data center scheduler and workload assignment in terms of quality of service, fault tolerance, and failure recovery, especially for latency critical online services. In this paper, we explore various characteristics of co-allocated online services and batch jobs from a production cluster containing 1.3k servers in Alibaba Cloud. From the trace data, we find the following: 1) For batch jobs with multiple tasks and instances, 50.8% failed tasks wait and halted after a very long time interval when their first and the only one instance fails. This wastes much time and resources as the remaining instances are running for an impossible successful termination. 2) For online services jobs, they are clustered in 25 categories according to their requested CPU, memory, and disk resources. Such clustering can help the co-allocation of online services jobs with batch jobs. 3) Servers are clustered into seven groups by CPU utilization, memory utilization, and their correlations. Machines with a strong correlation between CPU and memory utilization provides an opportunity for job co-allocation and resource utilization estimation. 4) The MTBF (mean time between failures) of instances are in the interval [400, 800] seconds while the average completion time of the 99th percentile is 1003 seconds. We also compare the cumulative distribution functions of jobs and servers and explain the differences and opportunities for workload assignment between them. Our findings and insights presented in this paper can help the community and data center operators better understand the workload characteristics, improve resource utilization, and failure recovery design.
Lexicon Based Sentiment Comparison of iPhone and Android Tweets During the Iran-Iraq Earthquake

Due to the vast amount of digital information on social media, a platform like Twitter provides a rich corpus for data mining in general, and opinion mining and sentiment analysis in specific. Moreover, and since social data is mostly unstructured in nature, effective data processing and analytics techniques are needed. The literature on sentiment analysis outlines three main approaches: supervised, unsupervised, and hybrid; each having its applications and advantages. After the November 2017 earthquake that hit the Iran-Iraq border, 50000 tweets were collected over a six-day period. The data was cleaned to isolate and study iPhone and Android tweets pertaining to sentiment and emotion detection. In this case study, unsupervised classification utilizing a lexicon based approach was used. The sentiments of iPhone and Android tweets were classified based on the polarity of the sentiment (i.e. negative or positive) as well as emotions. The objective of this study was to quantify the observed difference in sentiment between the Android and iPhone tweets using the NRC Word-Emotion Association lexicon.
A Validation of Web-based Surveys for Exploratory Research in the Areas of Business and Entrepreneurship

In this study, we demonstrate that web-based surveys are suitable for data collection in academic Business-related research. Using one of our datasets from an online study on entrepreneurial orientation, we investigated the construct validity and reliability of the instrument used to collect the data. Our analysis supports that for 28 Likert-scaled questionnaire items, a sample size of 332 people was adequate to conduct principle component analysis (PCA) and load the items into five components that are supported by literature. Cronbach's alpha was consistently high $\pmb{(\alpha=0.92)}$, with no evidence that the reliability would increase if any of the survey items were dropped. We therefore conclude that for short web-based surveys ($\pmb{< 30}$ items), a sample size of $\pmb{> 300}$ is suitable for exploratory factor analysis.
Estimating Zero-Sequence Line Impedance and Fault Resistance Using Relay Data

Data recorded by intelligent electronic devices such as protective relays contain a wealth of valuable information that can be used beyond post-mortem analysis of fault events. The objective of this paper is to propose algorithms to estimate zero-sequence line impedance and fault resistance using protective relay data collected during short-circuit fault events. Protective relaying data may be available from one or both ends of the line. For the latter, they may be collected at different sampling rates with dissimilar fault time instants. Furthermore, they may be unsynchronized. This paper presents approaches which uses measurement data from one and both ends of the line. The efficacy of the proposed algorithms presented in this paper are demonstrated using a test case as well as field data. The error percentage of the zero-sequence line impedance magnitude estimated using the data from both ends of a two-terminal line was under 3% for all the test cases. The error percentage in estimating the fault resistance was less than 1% in the test cases presented.