Miracle-dz/UniFAR-FTUs huggingface dataset:
Mapping of Concepts in Program Comprehension

Summary Software system’s understanding is the essential part of the software maintenance. Documentation and program source code could be the first aspect in software system’s understanding and its inner work-flow. This understanding of code is known as Program Comprehension (PC). With the advancement in technology and rapid growth in industry, information systems require frequent changes. This change need to be implement a specific idea or behavior which is named as “Concept”. Concept (variable, class, method) is referred as a typical behavior of code performing specific functionality like a student enrollment in student portal. In large scale systems, to locate the concept in the code is a very challenging task which require massive amount of time if performed in conventional ways. The conventional ways mean start finding the location by going through each and every file of the code. Programmer tries to locate these concepts with the help of domain knowledge using concept location techniques like static, dynamic and textual concept location technique. In this paper, we focused on static concept location technique and proposed a hybrid model for concept location in program comprehension.
PD-L1 promotes anticancer immune responses via activation of natural killer cells

Boosting anticancer immunity by blocking immune checkpoints such as the programmed death-1 (PD-1) or its ligand (PD-L1) is a breakthrough anticancer therapy. However, many cancer patients do not respond well to immune checkpoint blockades (ICBs) alone. Here we show that low-dose pharmacological immunoactivators (e.g., SN38, topotecan, sorafenib, etc.) notably downregulate PD-L1 and upregulate FOXO3 expression in various human and murine cancer cell lines. In a mouse tumor model, low-dose SN38 treatment markedly suppresses tumor growth, reduces PD-L1 expression, and enhances FOXO3 expression in primary tumor specimens. SN38 therapy engages the tumor-infiltrating mouse NK1.1/CD49b/NKG2D-positive natural killer (NK) cells to attack tumor cells by inducing mouse IFN-γ and granzyme-B secretion in the tumor microenvironment (TME) in vivo. SN38 treatment also promotes tumor cell apoptosis in the TME. SN38 treatment significantly decreases STAT3-pY705 and IL-6 protein levels; FOXO3 is essential for SN38-mediated PD-L1 downregulation. Collectively, these findings may contribute to future translational or clinical investigations tackling difficult-to-treat cancers with immune-activating medicines or combined with ICB immunotherapy.
How Reinforcement Learning Systems Fail and What to do About It

Recent research has turned to Reinforcement Learning (RL) to solve challenging decision problems, as an alternative to hand-tuned heuristics. RL can learn good policies without the need for modeling the environment’s dynamics. Despite this promise, RL remains an impractical solution for many real-world systems problems. A particularly challenging case occurs when the environment changes over time, i.e. it exhibits non-stationarity . In this work, we characterize the challenges introduced by non-stationarity and develop a framework for addressingthemtotrainRLagentsinlivesystems.Suchagents mustexploreandlearnnewenvironments,withouthurting thesystem’sperformance,andrememberthemovertime.To thisend,ourframework(1)identifiesdifferentenvironments encounteredbythelivesystem,(2)exploresandtrainsasep-arateexpertpolicyforeachenvironment,and(3)employs safeguardstoprotectthesystem’sperformance.Weapplyour frameworktostragglermitigation,andevaluateitagainsta varietyofalternativeapproachesusingreal-world.Weshow thateachcomponentofourframeworkisnecessarytocope withnon-stationarity.
PV FED INTEGRATED DOUBLE BUCK-BOOST LED DRIVER

The Proposed Integrated double buck boost converter features two capacitors, two inductors with only one controlled switch.It is able to supply a solid-state lamp from the dc mains fed with PV panel.The converter mean time between failures can be increased with the cautious design of the converter by selecting the film capacitors. The designed Integrated double buck–boost(IDBB) converter will power the LED lamps with the renewable source i.e.,from the PV cell. The dc mains providing low output LED ripple current. The operation of the converter is similar to the cascad connection of two buck–boost converters,at which only one controlled switch is shared by the two stages.The proposed driver circuit is designed for 70W and MATLAB/Simulink software is used for implementation.
4.0. Intrarenal injection of silica mesoporous reduced renal toxicity induced by gentamycin in

https://doi.org/10.30574/gscbps.2022. 19.1. 0138 Abstract Introduction: Mesoporous silica (MS) has recently used in biomedical applications due to its distinctive physicochemical properties. Purpose: The study aims to investigate the efficacy of MS to reduce renal toxicity induced by gentamicin in rats. Methods: The rats were randomly divided into three groups (n=6 per group): Control group (injected with 0.9% saline i.p daily for 8 consecutive), Gentamycin (injected with gentamycin, 100 mg/kg bw i.p. for 8 consecutive days) and MS group (after gentamycin injection, rat injected into left kidney with MS; 70 μg/0.5 ml/ rat). Results: Ms Treatment caused significant decreases in creatinine, urea, uric acid, and MDA while it increased GSH and catalase levels. Conclusion: Intrarenal injection of MS improved renal function in rats suffering from acute kidney
Smart tools and service opportunities for child protection ecosystem in the future : case family view

To cite this Article: Meristö, T. ; Kantola, T. ; Lankinen-Lifländer, M. (2016) Smart tools and service opportunities for child protection ecosystem in the future: case family view. In Tiziana, Russo-Spena; Cristina Mele (Eds.) Proceedings What's ahead in service research? New perspectives for business and society. 26th Annual RESER Conference, September 8-10, Naples, Italy. Napoli: University of Naples Federiico II, 939-953.
Frend for Edge Servers: Reduce Server Number! Keeping Service Quality!

To keep pace with the exploding data volume raised from geographical distribution edge networks, more and more edge servers have been built in recent years. As the computing power and storage capacity are different on each server, requests have to be transferred from one server to another before finally being responded and returned back to users. Such server-to-server transmission naturally introduce non negligible latency, which inevitably affects the quality of service (QoS). To eliminate this transmission latency, Internet Service Providers (ISPs) are building or renting more edge servers (both computing servers and storage servers) to reduce transmission distance and enhance the server configuration, which brings great costs. Fortunately, through a large number of real trace analysis, we found that it is possible to reduce server number while keeping the QoS! In this paper, we first disclose three key characteristics from Kuaishou Company: (1) Unbalanced request frequencies on dif-ferent servers; (2) Imprecise latency measure on server-to-server transmission; (3) Nonlinear latency reduction to server number increment. Based on these findings, we propose a frequency-aware edge storage server deployment strategy Frend that is an improved Genetic Algorithm to optimize the number of edge storage servers by the internal diffusion capability that is a new latency measure called S2SL. Through a series of experiments using real application data, we demonstrate that while achieving the same S2SL, Frend can reduce the number of required edge storage servers by up to 56% compared with the state-of-the-art Anveshak method.
THE IMPACT OF LEADERSHIP STYLE ON GROUP EFFECTIVENESS: THE MEDIATING ROLE OF COUNTERPRODUCTIVE BEHAVIOR

This research is an attempt to solve the Dilemma of leadership regarding the behavior of leaders towards their subordinates. Most of the previous literature on the leadership suggests that the leaders should differentiate their behaviors depending upon the needs of subordinates as well as their level of maturity. But the current research suggests the differentiation of behaviors on the part of leaders hampers group effectiveness. Furthermore this differentiation also results in increase in counterproductive behaviors of employees. The research data is gathered through structured questionnaires from 71 workgroups. The research tested a model addressing group focused Leadership as well as differentiated individual Focused leadership. Results of study revealed that differentiated Individual focused leadership is related negatively to group effectiveness through increasing counter productivity whereas group focused leadership relates positively to group effectiveness by decreasing counter productivity. Managerial as well as future implications of the research are also discussed.
Uwb Localization of People-Accuracy Aspects

The Time Of Arrival (TOA) localization technique in Ultra-Wideband (UWB) wireless sensor networks (WSN) is one of the most promising position location techniques that can be used to estimate the position of passive target objects like people. TOA technique determine the time that the signal takes from the transmitting antenna, the passive target object and the receiving antenna. TOA is then transformed into range distance. TOA algorithm involves solving a non linear equations resulting from estimated TOA ranges measured from multiple receiving antennas. The performance of four different passive TOA algorithms in wireless sensor networks is analyzed. The assessment and comparison of these algorithms has been made for two different simulation scenarios in Additive White Gaussian Noise (AWGN), where a passive target object needs to be localized. The simulation also considers a measure of accuracy and precision for TOA algorithms by applying the principal component analysis (PCA) to the covariance matrix of position estimates.
The Effect of Consumer Intrinsic Factors on Impulsive Buying Behavior in Online Marketplace: Case Study of Shopee Consumers

With the fast expansion of online purchasing, particularly in the online marketplace, a better knowledge of impulsive online purchases is becoming increasingly crucial. Using Shopee customers as a case study, this study examines the impact of elements in an intrinsic factor, such as personality traits, shopping enjoyment tendency, cultural traits, materialism, and impulsive buying tendency, on impulsive buying behavior in online marketplaces. The data processing and analysis methodologies in this study used PLS-SEM method. The online questionnaire was utilized to collect data from 273 consumers aged 18 and above who had or are presently using the Shopee online marketplace. A convenience sample approach was used to collect the sampling data. According to the findings of the PLS-SEM analysis, five of the ten hypotheses examined were accepted and had a significant influence on the variables. The intrinsic variables of consumer personality trait neuroticism, personality trait conscientiousness, shopping enjoyment tendency, materialism, and impulsive buying tendency all have a substantial impact on impulsive buying behavior. In addition, the findings include 5 hypotheses that were rejected. This research has managerial implications, including numerous activities for online marketplace marketing to define the target market and stimulate impulsive purchase behavior in customers based on their intrinsic factors.
Prediction of Communication Structures Based on Product Structures

Due to market pressure and increased collaborative work many companies face challenges arising from product and process complexity. The challenges include changes and iterations as well as cost and schedule overruns. Product and process of complexity are highly interlinked and can be tackled be structure-based models. It is commonly accepted that the process has to adapt to the product. Otherwise problems like quality and schedule issues arise. In this paper we test if the communication structure of a project can be predicted based on the product structure. We use the development of an electrically powered go cart as case study and derive communication and product structure independently for unbiased comparisons. We find that 86% of the communication relations can be predicted. Yet 44% of the predicted relations did not occur in the project.
Design and Implementation of Control Techniques for Differential Drive Mobile Robots: An RFID Approach

Localization and motion control (navigation) are two major tasks for a successful mobile robot navigation. The motion controller determines the appropriate action for the robot’s actuator based on its current state in an operating environment. A robot recognizes its environment through some sensors and executes physical actions through actuation mechanisms. However, sensory information is noisy and hence actions generated based on this information may be non-deterministic. Therefore, a mobile robot provides actions to its actuators with a certain degree of uncertainty. Moreover, when no prior knowledge of the environment is available, the problem becomes even more difficult, as the robot has to build a map of its surroundings as it moves to determine the position. Skilled navigation of a differential drive mobile robot (DDMR) requires solving these tasks in conjunction, since they are inter-dependent. Having resolved these tasks, mobile robots can be employed in many contexts in indoor and outdoor environments such as delivering payloads in a dynamic environment, building safety, security, building measurement, research, and driving on highways. This dissertation exploits the use of the emerging Radio Frequency IDentification (RFID) technology for the design and implementation of cost-effective and modular control techniques for navigating a mobile robot in an indoor environment. A successful realization of this process has been addressed with three separate navigation modules. The first module is devoted to the development of an indoor navigation system with a customized RFID reader. This navigation system is mainly pioneered by mounting a multiple antenna RFID reader on the robot and placing the RFID tags in three dimensional workspace, where the tags’ orthogonal position on the ground define the desired positions that the robot is supposed to reach. The robot generates control actions based on the information provided by the RFID reader for it to navigate those pre-defined points. On the contrary, the second and third navigation modules employ custom-made RFID
Classiﬁcation of Acoustic Scenes Using Convolutional Neural Networks

In deep learning, a convolutional neural network (CNN, or ConvNet) is a class of deep neural network, inspired by biological processes in that the connectivity pattern be-tween neurons resembles the organization of the animal visual cortex. Individual cortical neurons respond to stimuli only in a restricted region of the visual ﬁeld known as the receptive ﬁeld. The receptive ﬁelds of different neurons partially overlap such that they cover the entire visual ﬁeld. In recent years convolutional neural networks are growing exponentially in the ﬁelds of computer vision, natural language processing and speech recognition. However, there is a dearth of research on detection and classi-ﬁcation of acoustic scenes and events. In combination with IoT and wireless sensor networks, convolutional neural networks could help to characterize acoustic scenes and therefore better address noise issues present in urban environments. This paper investigates the theory and construction of convolutional neural networks for classiﬁcation of acoustic scenes like streets, supermarkets and cafes. A convolutional neural network architecture is proposed for our task. The inputs to the networks are time-frequency patches extracted from the computed mel-spectrogram of the signals. Dropout[1] and weight decay regularization mothods are applied and the cross-entropy loss is optimized using Adam algorithm.
Low-temperature thermal expansion of the topological material candidates β -PtBi 2 and β -Bi 2 Pd

We report on the low-temperature ( T < 120 K) thermal expansion of the bismuth-based topological semimetal β -PtBi 2 and topological superconductor β -Bi 2 Pd candidates. The linear thermal-expansion coeﬃcient of tetragonal β -Bi 2 Pd shows a pronounced anisotropy between the a - and c -axis while the volume thermal-expansion coeﬃcient α V ( β -Bi 2 Pd) is considerable larger than α V ( β -PtBi 2 ). The coeﬃcient α V ( β -PtBi 2 ) nearly matches the experimental speciﬁc heat, from which a Debye temperature θ D = 199 K is obtained. On the other hand, α V ( β -Bi 2 Pd) reasonably ﬁts the Debye model with θ D = 138 K, extracted from the low-temperature speciﬁc heat. An almost constant Gr¨uneisen parameter Γ ≈ 2 is obtained for both compounds. No magnetostriction is observed in any of both compounds up to µ 0 H = 16 T. We compare our results with other Bi-based topological materials.
Electrical Properties of a Ga 0.952 In 0.048 N 0.016 As 0.984 p-i-n Schottky Barrier Diode

The electrical characteristic of a Ga 0.952 In 0.048 N 0.016 As 0.984 p-i-n diode with quantum wells was characterized by current-voltage (I-V) measurement at room temperature. The electrical parameters of the diode such as a leakage current ( 𝐼 𝑜 ), the threshold voltage (V T ) and dynamic resistance ( 𝑅 𝑑 ) were extracted from the I-V characteristics. The p-i-n diode has a leakage current of 0.010 µA, threshold voltage of 0.22 V and dynamic resistance of 262 kΩ. Moreover, the Schottky parame ters of the Ti-Au/n-GaAs p-i- n diode such as an ideality factor (η), a Schottky barrier height (Φ b ) and series resistance (R s ) were determined using thermionic emission (TE) analysis of the forward-bias I-V characteristic as well as Norde and Cheung functions. The Schottky diode parameters showed an ideality factor of 4.79 with a barrier height of 0.79 eV were obtained using forward-bias I-V characteristics. On the other hand, barrier heights determined from the Norde and Cheung functions were calculated at 0.87 eV and 0.71 eV, respectively. As there is an inconsistency between the current-voltage characteristics and the models, the necessity to investigate the temperature dependence diode parameters for accurate determination of the mechanism responsible for the charge transfer and Schottky barrier height of GaInNAs p-i-n diode.
Geometrically nonlinear configurations in rod-like structures

Rod-like structures are ubiquitous in both nature (e.g. bacterial flagella) and engineering (ropes, cables, pipelines, and Carbon nanotubes), from nanometer to kilometer scale, and often undergo geometrically nonlinear deformation. Due to the slender geometry of the structures, the material strain usually remains in linear elastic limit even though the deformed geometry deviates significantly from the original geometry. The geometric nonlinearities that result from the deformation process pose enormous challenges to traditional analytical and numerical tools. Moreover, it is often unfeasible to perform experiments at the original length-scale of these systems. We overcome these challenges by combining model experiments with cutting-edge computational tools ported from computer graphics, and theoretical analysis towards developing predictive physical understanding of these systems. The prominence of geometry in this class of systems enables the scaling (up or down) of the problem to the desktop scale, which allows for systematic experimental exploration of parameter space. In parallel, we conduct numerical simulations using the Discrete Elastic Rods (DER) method, which was originally developed for the animation industry for special effects of the visually dramatic dynamics of hair, fur, and other rod-like structures. We port DER into engineering as a predictive computational tool and test ride it against our own model experiments. A collection of problems from three a priori unrelated scenarios, at disparate length-scales, are explored. First, as a model for laying of submarine cables onto the seabed (kilometer scale), we consider deployment of elastic rods onto a moving substrate (conveyor belt) and quantify the resulting nonlinear coiling patterns. The DER method is employed to identify the phase boundaries between different patterns and characterize the morphology. Our results are interpreted using a reduced geometric model for the evolution of the position of the contact point with the belt and the curvature of the rod in its neighborhood. This geometric model reproduces all of the coiling patterns, which allows us to establish a universal link between our elastic problem and the analogous patterns obtained when depositing a viscous thread onto a moving surface; a well-known system referred to as the fluid mechanical sewing machine. Secondly, we consider a macroscopic analog model for the locomotion of uni-flagellar bacteria
Congested Scene Analysis using Dilated Convolutional Generative Adversarial Networks

This paper proposes a deep-learning based novel crowd counting network to estimate the number and understand the distribution of people in densely crowded images by producing high-quality density maps. This application of semantic segmentation methods is very important in controlling the problems that arise from overcrowding including particularly dangerous ones like stampedes and collapsing of structures. The generator model in our GAN-based approach consists of kernels with varying dilation rates leading to aggregation of information from diﬀerent-sized receptive ﬁelds. Our proposed approach not only produces high quality density maps but achieves a signiﬁcant parameter reduction compared to other contemporary methods. We have tested our approach on the ShanghaiTech dataset and have achieved very good results for both: part A and part B while using our model of size 511 kB - achieving a size reduction of more than 500 percent when compared to the state-of-the-art model.
MSFNET: Multi-Stage Fusion Network for Semantic Segmentation of Fine-Resolution Remote Sensing Data

This work proposes a Multi-Stage Fusion Network (MSFNet) for semantic segmentation of fine-resolution remote sensing data by exploiting a multi -stage transformer architec-ture. The proposed MSFNet fuses information of different scales and modalities using a multi-stage scheme based on cross-attention mechanism. More specifically, the proposed MSFNet is composed of two Multi-Level Transformers (ML-Trans), one Crossmodal Fusion Transformer (CFTrans) and one Global-Context Augmented Transformer (GCATrans). DMLTrans and CFTrans are designed to fuse features in dif-ferent levels in each modality and high-level crossmodal ab-stract features, respectively, whereas GCATrans enhances the fusion feature of the main modal. Capitalizing on MSFNet, this work demonstrates the fusion of red-green-blue (RGB) remote sensing images and digital surface model (DSM) data. Extensive experiments on large-scale fine-resolution remote sensing data sets, namely the ISPRS Vaihingen, confirm the excellent performance of the proposed architecture as compared to conventional multimodal methods.