Miracle-dz/UniFAR-FTUs huggingface dataset:
Low-Latency and Energy-Balanced Data Transmission Over Cognitive Small World WSN

Energy balancing and faster data transfer over a wireless sensor network (WSN) is an important problem in applications like cyber-physical systems, Internet of things, and context-aware pervasive systems. Addressing this problem leads to increased network lifetime and improved network feasibility for real time applications. In WSNs, sensor nodes transfer the data using multihop data transmission model. The large number of hops required for data transmission leads to poor energy balancing and large data latency across the network. In this paper, we utilize a recent development in social networks called small world characteristics for proposing a novel method of low-latency and energy-balanced data transmission over WSN. Small world WSN (SW-WSN) exhibits low average path length and high average clustering coefficient. A cognitive SW-WSN is developed by adding new links between a selected fraction of nodes and the sink. A new data routing method is also proposed by optimizing energy cost of the links. This method yields uniform energy consumption and faster data transfer. Experiments are conducted using simulations and real node deployments over a WSN testbed. The performance of the proposed method is evaluated by conducting exhaustive analysis of network lifetime, residual energy, and data latency over the WSN. Experimental results obtained indicate that the proposed cognitive small world model achieves energy balancing, increases network lifetime, improves energy efficiency, and reduces data latency when compared to results obtained using various state-of-the-art approaches. The results are motivating enough for the proposed method to be used in large and medium scale network applications.
A Study of Software Testing Practices in Sri Lankan Software Companies

Software testing is a knowledge intensive and a collaborative activity, which mainly depends on knowledge and experience of the software testers. This study presents using an empirical investigation carried out in Sri Lankan software companies about the current status of software testing practices. The study focused on mainly testing methodologies and techniques, automated tools, testing metrics, testing training and academic collaboration with software industry. As reported from this study, Agile methodology became very popular in software companies for software development. As an overall analysis, there was a great interest in using tools such as Selenium for testing activities. Findings of this study emphasized that while software testing practices were considered to be important, training, money and resources were inadequate to address an effective management of software testing knowledge in these three software companies. Findings also presented a compelling evidence of the soundness for the need of knowledge management practices to manage software testing knowledge,
The Relationship Among Morningness-Eveningness, Sleep Duration, Social Jetlag, and Body Mass Index in Asian Patients With Prediabetes

Background: Circadian system is known to influence energy metabolism. Recent evidence suggested that evening preference could be associated with higher body mass index (BMI). Moreover, evening preference is known to be associated with insufficient sleep duration and greater social jetlag, both described to be associated with obesity. This study aimed to explore whether morningness-eveningness was directly associated with BMI or its effect was transmitted through sleep duration or social jetlag in patients with prediabetes. Methods: A total 2,133 patients with prediabetes were enrolled. Morningness-eveningness was assessed using a Composite Scale of Morningness (CSM). Average weekly sleep duration and sleep timing were obtained, and social jetlag was calculated. BMI was calculated by weight (kg)/height2 (m2). A mediation analysis was performed based on two pathways, i.e. CSM→sleep→duration→BMI and CSM→social jetlag→BMI. A sequential equation model was used to estimate the direct and indirect effects of CSM on BMI. Results: Mean (SD) age and BMI were 63.6 (9.2) years and 25.8 (4.0) kg/m2. For CSM→sleep duration→BMI pathway, every one point decrease in CSM (more evening preference) was associated with a decrease in sleep duration by 0.054 h (95% CI 0.043–0.066), whereas sleep duration was negatively associated with BMI (coefficient = −0.156, 95%CI −0.288, −0.024). Mediation analysis indicated that a change in CSM (from 90th to 10th percentile, more evening preference) was associated with a decrease in sleep duration and an increase in BMI by 0.102 kg/m2 (95% CI 0.015, 0.207). In addition, this change in CSM was directly associated with an increase in BMI by 0.511 kg/m2 (95%CI 0.030, 0.952). The CSM→social jetlag→BMI pathway analysis revealed that social jetlag was not significantly associated with BMI. A subgroup analysis in those aged ≤60 years (n = 784) revealed that each hour increase in social jetlag was associated with an increase in BMI by 0.56 kg/m2 (p = 0.026) while CSM and sleep duration were not. Conclusion: In patients with prediabetes, more evening preference was directly associated with higher BMI and indirectly through insufficient sleep duration, while social jetlag did not mediate the relationship between CSM and BMI. In those ≤60 years, only greater social jetlag was associated with higher BMI. These data could inform further interventional studies to reduce BMI in this high risk group.
Stealth Jamming Attack in WSNs: Effects and Countermeasure

This paper explores a novel type of attack in wireless sensor networks-Stealth Jamming (SJAM), in which malicious nodes periodically generate bogus traffic in order to trigger false route breakages. More precisely, they target a relatively unexplored weakness inherent in the route maintenance mechanism of ad hoc protocols used by trust-aware routing schemes. Missing a certain number of transmission attempts at the data link layer is typically considered as the link, and consequently route breakage. In this attack, malicious nodes silently listen to the wireless channel, and if the busy airtime (indicative of wireless congestion) exceeds a certain threshold, it starts injecting gratuitous fake broadcast messages for a brief duration, thereby leading to excessive collisions for a transient period and leading to failed transmissions and route breakages, significantly degrading network performance. SJAM is difficult to detect, because the malicious nodes are active only if congestion exceeds a threshold and then only periodically. It is difficult to discern these attacks from genuine route breakages, which are also quite common. In this paper, we explore this type of attack and propose an intelligent cross-layered scheme for mitigating these attacks, thereby improving route stability, throughput, and delay performance. We discuss how enhanced route maintenance in existing trust-aware routing solutions ensures wide applicability of the solution. Simulation results demonstrate the improved network performance when compared with the state-of-the-art solutions.
Summarization of Scientific Paper Through Reinforcement Ranking on Semantic Link Network

The semantic link network is a semantics modeling method for effective information services. This paper proposes a new text summarization approach that extracts semantic link network from scientific paper consisting of language units of different granularities as nodes and semantic links between the nodes, and then ranks the nodes to select Top-k sentences to compose summary. A set of assumptions for reinforcing representative nodes is set to reflect the core of paper. Then, semantic link networks with different types of node and links are constructed with different combinations of the assumptions. Finally, an iterative ranking algorithm is designed for calculating the weight vectors of the nodes in a converged iteration process. The iteration approximately approaches a stable weight vector of sentence nodes, which is ranked to select Top-k high-rank nodes for composing summary. We designed six types of ranking models on semantic link networks for evaluation. Both objective assessment and intuitive assessment show that ranking semantic link network of language units can significantly help identify the representative sentences. This paper not only provides a new approach to summarizing text based on the extraction of semantic links from text but also verifies the effectiveness of adopting the semantic link network in rendering the core of text. The proposed approach can be applied to implementing other summarization applications such as generating an extended abstract, the mind map, and the bulletin points for making the slides of a given paper. It can be easily extended by incorporating more semantic links to improve text summarization and other information services.
Economic Dispatch Using Functional Network Wind Forecast Model

This paper targets at the application of a realtime forecasting model in profitable power dispatch and the study of the impact of forecast error for energy management in terms of system costs and profits. For this purpose, a novel method based on a modern artificial intelligence paradigm, called functional network is presented in this paper for the development of a multi-step forecasting model. Wind power and market price forecasts obtained from the developed model are incorporated in a receding horizon optimization policy to maximize the running income and operational profits of a simulated microgrid with wind generation and battery energy storage system (BESS). The utilization of accurate forecast information not only enables a smooth operation but also helps in optimal BESS sizing. It is also observed that the developed forecast model, being more accurate, results in an improved income and profit for selling the energy. Hence the proposed work can have profound implications for wind power industry and energy markets as it can greatly enhance the economics of energy management and dispatch schemes.
Multilook SAR Image Enhancement Using the Dual Tree Complex Wavelet Transform

We discuss in this paper the enhancement of multilooks SAR images for the purpose of wave detection. The method is based on improving the contrast in the dual tree complex wavelet domain, while performing denoising simultaneously. We describe the particularities of Sentinel-1 Strip map multilooks ground range detected images with high resolution and present the advantages of the proposed SAR image enhancement method. Simulation results and comparisons with state of the art contrast enhancement and denoising methods highlight the benefits of the proposed solution.
Rope Deployment Method for Ropeway-Type Vermin Detection Systems

In recent years, damage to rural areas by vermin such as deer, wild boars or monkeys has increased in both frequency and severity. This problem is expected to be counteracted by wireless sensor networks constructed from multiple sensor nodes with wireless communication devices. These systems reduce the damage by detecting vermin and repelling them by signals such as sounds and light. However, owing to their fixed monitoring cameras, general monitoring systems cannot always cope with plant growth and other obscurations that decrease the monitored area. This paper proposes a ropeway-type vermin detection system that moves the monitoring cameras on ropes, and a method that minimizes the number of required ropes in the expected monitoring scenario. For efficient monitoring with as few cameras as possible, the method groups several target areas into one by a clustering procedure. The grouped area can then be monitored from a single position. Subsequently, our algorithm finds the most efficient rope deployment that completely monitors the grouped areas. In simulations, the proposed method monitored all target areas with 26% fewer monitoring cameras than a general clustering method (k-means clustering).
Head Pose Estimation Using Deep Architectures

Head pose estimation is an important step in many applications related to face analysis, human-computer interaction or activity and behavoiur analysis. We investigate the problem of head pose estimation under a Deep Convolutional Neural Networks (DCNN) framework. Making use of the recent advances in the mentioned field, we test different DCNN architectures and thoroughly evaluate them on UPNA database. We show that adding additional information about location of face fiducial points can increase the accuracy of continuous head pose estimation.
A FPGA Based SAT Solver with High Random and Concurrent Strategies

Various types of SAT solvers are widely used in model checking for ensuring the reliability of software and hardware systems. Currently, many SAT solvers have been developed and most of them are implemented by means of software manners such as MiniSAT. However, the software-based SAT solvers are often not capable of dealing with the complex systems that hold a large number of states and transitions. To solve this problem, the hardware-based SAT solvers have attached much attention in industry because of its high-performance including efficiency and scalability. In this paper, we develop a SAT solver based on the hardware FPGA. In the solver, an improved DPLL algorithm is embedded in FPGA to solve satisfiable problems. We have evaluated our SAT solver based on many experiments with a lot of standard benchmarks. The experimental results show that our solver is an efficient and scalable SAT solver compared with the existing ones.
On the Maximal Achievable Rate for Signal-Code Construction Based on Interleaved Reed-Solomon Codes for Multiple Access System Over Vector-Disjunctive Channel

We consider a signal-code construction for a special class of multiple access system over vector-disjunctive channel. This construction is based on interleaved Reed-Solomon codes with collaborative decoding. The considered methods of encoding and decoding on one hand have acceptable complexity for a wide range parameters and on other one a decoding algorithm is able to correct significantly more collisions than bounded-distance decoder. We estimate a maximum achievable relative sum-rate of this construction for fixed number of active users and total number of frequencies to transmit.
PCG Classification Using Multidomain Features and SVM Classifier

This paper proposes a method using multidomain features and support vector machine (SVM) for classifying normal and abnormal heart sound recordings. The database was provided by the PhysioNet/CinC Challenge 2016. A total of 515 features are extracted from nine feature domains, i.e., time interval, frequency spectrum of states, state amplitude, energy, frequency spectrum of records, cepstrum, cyclostationarity, high-order statistics, and entropy. Correlation analysis is conducted to quantify the feature discrimination abilities, and the results show that “frequency spectrum of state”, “energy”, and “entropy” are top domains to contribute effective features. A SVM with radial basis kernel function was trained for signal quality estimation and classification. The SVM classifier is independently trained and tested by many groups of top features. It shows the average of sensitivity, specificity, and overall score are high up to 0.88, 0.87, and 0.88, respectively, when top 400 features are used. This score is competitive to the best previous scores. The classifier has very good performance with even small number of top features for training and it has stable output regardless of randomly selected features for training. These simulations demonstrate that the proposed features and SVM classifier are jointly powerful for classifying heart sound recordings.
Beam-Tracking Technique for Millimeter-Wave Cellular Systems Using Subarray Structures

In this paper, a beam-tracking technique for orthogonal frequency division multiplexing (OFDM)-based millimeter-wave cellular systems is proposed using subarray structures and pilot sequences. In order to track the variations in the angle of departure (AoD) and angle of arrival (AoA), an antenna array is configured into two different subarrays, which allow us to estimate AoD/AoA based on the phase difference between the two subarrays in the received signals. A pilot sequence is designed such that it can distinguish between different subarrays and track a beam for a mobile station in multicell multiuser environments. Two different implementation methods of AoD/AoA estimation are proposed and compared with computer simulations.
SoundAuth: Secure Zero-Effort Two-Factor Authentication Based on Audio Signals

Two-factor authentication (2FA) popularly works by verifying something the user knows (a password) and something she possesses (a token, popularly instantiated with a smart phone). Conventional 2FA systems require extra interaction like typing a verification code, which is not very user-friendly. For improved user experience, recent work aims at zero-effort 2FA, in which a smart phone placed close to a computer (where the user enters her username/password into a browser to log into a server) automatically assists with the authentication. To prove her possession of the smart phone, the user needs to prove the phone is on the login spot, which reduces zero-effort 2FA to co-presence detection. In this paper, we propose SoundAuth, a secure zero-effort 2FA mechanism based on (two kinds of) ambient audio signals. SoundAuth looks for signs of proximity by having the browser and the smart phone compare both their surrounding sounds and certain unpredictable near-ultrasounds; if significant distinguishability is found, SoundAuth rejects the login request. For the ambient signals comparison, we regard it as a classification problem and employ a machine learning technique to analyze the audio signals. Experiments with real login attempts show that SoundAuth not only is comparable to existent schemes concerning utility, but also outperforms them in terms of resilience to attacks. SoundAuth can be easily deployed as it is readily supported by most smart phones and major browsers.
The effect of childhood deprivation on weight status and mental health in childhood and adolescence: longitudinal findings from the Millennium Cohort Study.

Background The study aims were to: (i) examine associations between deprivation at age 7 and health outcomes at age 7 and 14, (ii) determine whether a deprivation gradient to health outcomes exists at age 7 and 14, and (iii) assess the extent to which health outcomes at age 7 are associated with health outcomes at age 14. Methods Data were from wave four and six of the Millennium Cohort Study. Health outcome measures were weight status, and Strengths and Difficulties Questionnaire measured mental health problems. Deprivation was determined using the 2004 English Indices of Multiple Deprivation. Adjusted logistic and multinomial logistic regressions were conducted. Results A total of 6109 children (1890 girls) had complete data. Overweight, obesity and mental health problems were greatest among children in the highest deprivation decile at age 7 and 14 (P < 0.001). Health outcomes at age 7 were significantly associated with health outcomes at age 14 (P < 0.001). Conclusions A marked social gradient to weight status and mental health was evident at age 7 and 14, and no evidence of equalization was found. Weight status and mental health in childhood is strongly associated with weight status and mental health in adolescence.
Vicinal land use change strongly drives stream bacterial community in a tropical montane catchment

Impact of land use (LU) change on stream environmental conditions and the inhabiting bacterial community remains rarely investigated, especially in tropical montane catchments. We examined the effects of LU change and its legacy along a tropical stream by comparing seasonal patterns of dissolved organic carbon (DOC) / colored dissolved organic matter (CDOM) in relation to variations in structure, diversity and metabolic capacities of particle-attached (PA) and free-living (FL) bacterial communities. We hypothesized that despite seasonal differences, hydrological flows that accumulate allochthonous carbon along the catchment are a major controlling factor of the bacterial community. Surprisingly, local environmental conditions that were largely related to nearby LU and the legacy of LU change were more important for stream bacterial diversity than hydrological connectivity. DOC was strongly correlated with PA richness and diversity. The legacy of LU change between teak plantation and annual crops induced high DOC and high diversity and richness of PA in the adjacent waters, while banana plantations were associated with high diversity of FL. The community structures of both PA and FL differed significantly between seasons. Our results highlight the importance of vicinal LU change and its legacy on aquatic bacterial communities in mixed used tropical watersheds.
Learning Recursion from Music and Music from Recursion

Recursion is a fundamental concept in several courses in computer science but is often one of the most confusing concepts for beginners. On the other hand, music is universal and appeals to most people. This paper is a novel attempt to understand and utilize the synergy between music and computer science to teach recursion to freshman students. The paper presents the retrograde strategy and collaboration techniques used during the composition of music and explains how these ideas can be used to learn recursion and stack data structure. We taught recursion to 30 first year undergraduate students in computer science using the normal approach and then demonstrated the control flow using the retrograde strategy. We found that around 81.2% preferred correlation with music. We see this as an initial step towards a deeper correlation of composing music and composing code.
A Novel Non-Isolated Active Charge Balancing Architecture for Lithium-Ion Batteries

Active charge balancing is an approved technique to implement high performance lithium-ion battery systems. Enhanced balancing speeds and reduced balancing losses are feasible compared to passive balancing. The new architecture proposed in this paper overcomes several drawbacks of other active balancing methods. It consists of only 2 non-isolated DC/DC converters. In combination with a MOSFET switch matrix it is able to balance arbitrary cells of a battery system at high currents. Adjacent cells can be balanced simultaneously. For the given setting, numerical simulations show an overall balancing efficiency of approx. 92.5%, compared to 89.4% for a stack-to-cell-to-stack method (St2C2St, bidirectional fly-back) at similar balancing times. The usable capacity increases from 97.1% in a passively balanced system to 99.5% for the new method.