Miracle-dz/UniFAR-FTUs huggingface dataset:
Task-related Information Sharing in Group Decision Support Systems (GDSS) The Importance of Knowing Who Knows What

Group Decision Support Systems (GDSS) are among the most common software systems designed to enable and enhance group communication for collective decision-making. Members of a work group normally confer and exchange information in order to make decisions and there are three types of task-related information: common (or shared) information known by all members of a work group; unique (or unshared) information known by only one group member; and partially shared information known by more than one but not all members. To date, only a handful of studies have evaluated information exchange in GDSS groups engaged in decision-making and this study examines the effect of expertise role-assignment and the proportion of common, unique, and partially shared information available in GDSS groups. The results indicate that information distribution and expertise role-assignment can affect information sharing in GDSS groups. Increasing the proportion of unique information distribution can be used as a way to enhance the pooling of unique information in expertise role-assigned groups. Expertise role-assignment increases the amount of partially shared information during group discussions and increasing the proportion of unique information results in an increase in the retaining of partially shared information but decreases the retaining of unique information following group discussions.
Motor-Driven Polygon Mirrors for Measurements of Nano-Second Phenomena

Measurement and control of rapid optical processes usually require pulsed lasers or fast-response instruments, which are too expensive or complicated for use in industries. A motor-driven polygon mirror can deflect an optical beam so that it passes across a distance of 1 mm in 5 ns, i.e., a sweep velocity of 190 km/s. This time-space conversion realizes rapid modulation of a continuous-wave beam and observation of nano-second phenomena. This article reports some experimental results that were attained by the use of this sweep method.
Supporting Group Cognition in an Online Math Community: A Cognitive Tool for Small-Group Referencing in Text Chat

The Virtual Math Teams Project is exploring how to create, structure, support, and assess an online chat-based collaborative community devoted to mathematics discourse. It is analyzing the forms of group cognition that emerge from the use of shared cognitive tools with specific functionalities. Centered on a case study of a synchronous online interchange, this article discusses the use of a graphical referencing tool in coordination with text chat to achieve a group orientation to a particular mathematical object in a shared whiteboard. Deictic referencing is seen to be a critical foundation of intersubjective cognitive processes that index objects of shared attention. The case study suggests that cognitive tools to support group referencing can be important to supporting group alignment, intentionality, and cognition in online communities such as this one for collaborative mathematics.
Spatial Cognition 2004: Poster Presentations

Previous research on landmark has focus on landmark function in navigation. However, it is still not clear how a landmark is identified in a visual scene. Studies on active vision show that eye movements are proactive, anticipating actions. Our eyes actively gather task-relevant information from the visual scene. Here we are interested in how eye movements involve in landmark recognition in navigation. We analyzed eye movements of human subjects with respect to landmarks and distractors when they performed a navigation task in a virtual reality environment. Landmarks are fixated longer and more frequently than other objects in the scene.
The Analysis of a Ferriteless Rectangular Coupler With Reactive Assistive shielding Coils For EV Wireless Charging

Autonomous driving technology has made significant progress in recent decades. For fully autonomous driving of an electric vehicle, the recharging process should be possible without a manually fixed connection. Wireless charging technology is a promising solution for electric vehicle recharging automation. The wireless transformer/coupler is the key component in electric vehicle wireless charging. The maximum power transfer capability and efficiency are limited by the coupler. To reach the required power level, efficiency and electromagnetic exposure to surrounding area, the coupler should be analyzed. In this paper, a coupler with assistive coils is presented and analyzed. The assistive coils perform as reactive shielding coils as well as flux concentrating coils. Finite element simulation and circuit simulation results are shown.
Sarcasm Detection on Indonesian Twitter Feeds

In social media, some people use positive words to express negative opinion on a topic which is known as sarcasm. The existence of sarcasm becomes special because it is hard to be detected using simple sentiment analysis technique. Research on sarcasm detection in Indonesia is still very limited. Therefore, this research proposes a technique in detecting sarcasm in Indonesian Twitter feeds particularly on several critical issues such as politics, public figure and tourism. Our proposed technique uses two feature extraction methods namely interjection and punctuation. These methods are later used in two different weighting and classification algorithms. The empirical results demonstrate that combination of feature extraction methods, tf-idf, k-Nearest Neighbor yields the best performance in detecting sarcasm.
NBBS: A Non-Blocking Buddy System for Multi-core Machines

Common implementations of core memory allocation components, like the Linux buddy system, handle concurrent allocation/release requests by synchronizing threads via spin-locks. This approach is not prone to scale, a problem that has been addressed in the literature by introducing layered allocation services or replicating the core allocators—the bottom most ones within the layered architecture. Both these solutions tend to reduce the pressure of actual concurrent accesses to each individual core allocator. In this article we explore an alternative approach to scalability of memory allocation/release, which can be still combined with those literature proposals. We present a fully non-blocking buddy-system, where threads performing concurrent allocations/releases do not undergo any spin-lock based synchronization. Our solution allows threads to proceed in parallel, and commit their allocations/releases unless a conflict is materialized while handling the allocator metadata. Conflict detection relies on atomic Read-Modify-Write (RMW) machine instructions. Beyond improving scalability and performance, our solution can also avoid wasting clock cycles for spin-lock operations by threads that could in principle carry out their memory allocations/releases in full concurrency.
Optimal rate allocation for progressive fine granularity scalable video coding

We examine the enhancement-layer rate allocation problem in progressive fine granularity scalable (PFGS) video coding. The problem arises from the fact that different frames in the enhancement layer have different rates in PFGS coding. A rate-distortion (R-D) function for a multi-frame group is first established for enhancement-layer PFGS coding, followed by experiments to verify its validity using real test sequences. Optimal rate allocation among frames in the group is then given based on the R-D function, together with a simple implementation that is suitable for applications like streaming video. Experiments show that, compared with uniform bit allocation, optimal bit allocation not only makes the quality variation in decoded video much smoother, but also improves the average PSNR of PFGS coding by 0.3-0.5 dB.
Byzantine-Robust Stochastic Gradient Descent for Distributed Low-Rank Matrix Completion

To overcome the growing privacy concerns of centralized machine learning, federated learning has been proposed to enable collaboratively training a model with data stored locally in the owners’ devices. However, adversarial attacks (e.g., Byzantine attacks in the worst case) still exist in the federated learning systems so that the information shared by the data owners are unreliable. Byzantine-robust aggregation methods, such as median, geometric median and Krum, have been found to perform well in eliminating the negative effects caused by the Byzantine attacks. In this paper, we study the distributed low-rank matrix completion problem in a federated learning setting, where some data owners are malicious. We combine the Byzantine-robust aggregation rules with stochastic gradient descent (SGD) to solve this problem. Numerical experiments on the Netflix dataset demonstrate that the proposed methods are able to achieve comparable performance relative to SGD without attacks.
Directly and Indirectly Synchronous Communication Mechanisms for Client-Server Systems Using Event-Based Asynchronous Communication Framework

In this paper, we propose a synchronous communication mechanism that can be used with asynchronous communication in an event-based communication framework (CM). The proposed synchronous communication mechanism supports directly synchronous communication between client and server, indirectly synchronous communication between two clients through server mediation, and indirectly one-to-many synchronous communication with multiple clients. In order to support synchronous communication, the CM uses the synchronization mechanism between the main thread and the processing thread while maintaining the multi-thread structure. The application can utilize both the asynchronous and the synchronous communication service of the CM together according to its requirements. For performance analysis, we compared the CM synchronous and asynchronous communication methods with qualitative and quantitative experiments, respectively. Through qualitative analysis, developing applications using synchronous services can more intuitively design application logic than asynchronous services. From the quantitative experiment, we also verified that the response delay time in the synchronous method is shorter than that in the asynchronous method although the difference in the response delay is not large.
Effects of Annealing Temperatures on the Electrochemical Properties of Silicon Carbide Anode Film for Lithium Ion Battery

The effects of annealing temperatures on the electrochemical properties of silicon carbide anode films are investigated for lithium-ion battery applications. The SiC film shows improved electrochemical performance with increasing annealing temperature. The 900 ° C-annealed SiC film exhibits a reversible capacity of 580 mAh g $^{-1}$ over 100 cycles. Moreover, the 900 ° C-annealed film displays better rate performance with less severe degradation $(\sim 12.2$%) than the other two films.
Study of security algorithms to secure IOT data in middleware

In the present generation internet plays a major role. The data being sent by the user is created by the things like pc, mobiles, sensors etc. and these data are sent to the cloud system. When a data from the IOT devices are sent to the cloud, there is a question of privacy and security. To provide security for the data well-known security algorithms are used in fog layer and are successful in transferring the data without any damage. Here different techniques used for providing security for IOT data are discussed.
Predicting Lung Cancer Using Datamining Techniques With the AID of SVM Classifier

The suggested techniques provide a noble quality tool to predict lung tumor classification and play a major role, particularly in the finding and classification of medical data. The literature reports a number of lung cancer diagnosis systems which predict normal and abnormal lung cancers with the support of SVM. Our proposed research focuses on predicting lung cancer whether it is normal or abnormal, with respect to the classification technique. Initially, in the preprocessing phase, suitable data from the input data set are extracted after preprocessing; the resultant output is fed to the feature selection. In this feature selection phase, the features are selected with the aid of the firefly algorithm. After the feature selection, the particular features are served in to the support vector machine (SVM) classifier; with the aid of this classifier, the data are classified as either normal or abnormal. The proposed method will be implemented in Matlab with various lung cancer data. In addition to this, our proposed work will be in comparison with the present strategies and algorithms for proving that our proposed work is the best one.
Rules-Based Integration of News-Trading Algorithms

We propose a rules-based approach to optimize news-trading systems, where each event is screened by a limited number of relevant text and data-analysis algorithms. Those are selected automatically from a set of executable components, and are integrated in iterative workflows based on knowledge or rules database of market and event conditions. Events may also be chained together to detect emerging patterns through time, and to provide performance benchmarks among comparable scenarios and strategies. We review recent advances in news trading, and present the architecture of a next-generation system for rules-based integration of algorithms that could provide more effective execution and fund management in the context of high-frequency trading (HFT).
Boundary scan access of built-in self-test for field programmable gate arrays

We discuss issues associated with system level access of Built-In Self-Test (BIST) for Field Programmable Gate Arrays (FPGAs) via the Boundary Scan Interface. In addition, we describe the design of an Application Specific Integrated Circuit (ASIC) which serves as an interface between a PC parallel port and the Test Access Port (TAP) of one or more FPGAs to reprogram the FPGA(s) and administer BIST during off-line testing. We also include a brief description of the FPGA BIST architecture and operation.
Optimized Water Consumption Considering Power Plants Efficiency in Power System Generation

In order to generate adequate electricity energy, conventional power plants utilize dramatic volume of water resources. Different methods such as replacing cutting-edge technologies of facilities have been proposed to diminish water consumption in this industry. Most of the approaches are considered as medium or long-term solution which requires substantial investment costs. Meanwhile, because of the process and technology, natural gas-fired power plants need less water among conventional power plants. Therefore, shifting unit commitment planning toward these power plants would aid to save water consumption. Nevertheless, overlooking combined cycle power plants can perform negative impact on power plant efficiency, and it may increase power generation cost. This paper aims to investigate multi-objective optimization problem to minimize water consumption by maximizing the power generation efficiency. Hence, this method contemplates economic dispatch by selecting more efficient and less water required power plants. The proposed model is examined on an IEEE 24-bus test system to address the results, and it is compared with the conventional economic dispatch problem to clarify the impact of the proposed approach. According to the results, some relevant conclusions are drawn.
Combined Wireless Network Intrusion Detection Model Based on Deep Learning

In order to effectively detect wireless network intrusion behavior, a combined wireless network intrusion detection model based on deep learning was proposed. First, a feature database was generated by feature mapping, one-hot encoding, and normalization processing. Then, we built a deep belief network (DBN) with the multi-restricted Boltzmann machine (RBM) and the back propagation (BP) network. The BP network layer was connected as an auxiliary layer to the end of the RBM. The back-propagation algorithm was used to fine-tune the weight of the multi-restricted Boltzmann machine. Finally, the support vector machine (SVM) was used to train the detection method. After training, the intrusion detection model, which had the DBN-SVM detection method, was determined. The experimental results show that the detection model has good intrusion detection performance.
Smart Frequency Control using Coordinated RFB and TCPS based on Firefly Algorithm

The frequency stability enhancement of a power system is proposed in this paper. To enhance the frequency stability, redox flow batteries (RFB) and the thyristor controlled phase shifter are used. Moreover, to get a better performance, the parameter of RFB and TCSC are optimized by the firefly algorithm (FA). Two area load frequency control plant is used as a test system. Time domain simulation is used to assess the performance of the proposed method (adding RFB and TCPS and optimized using FA). From the simulation results, it is found that by installing RFB and TCSC based on FA in the system, the frequency performance can be maintained above the nadir when perturbation emerges.
Semantic Segmentation of Buildings in Remote Sensing Images Based on Dense Residual Learning and Channel Adaption

Deep fully convolution neural network has opened a new field in semantic segmentation for remote sensing images. In this paper, an improved U-net model is proposed to extract buildings at the pixel level, so as to obtain its contour and size information. In this improved U-net model, the highly modular ResNeXt50 network is used as the encoder of U-net and a parallel dense residual module based on atrous convolution is proposed to extract multi-scale semantic information for segmentation. Moreover, the transposed convolution whose stride is 2 is used for upsampling to obtain the grayscale segmentation mask. This modified model adopts the sum of jaccard loss and binary cross entropy loss as the total loss function. Our experimental results have demonstrated that our improved U-net model has excellent performance on semantic information encoding and multi-scale feature extraction. Finally, the mean pixel precision (MPA), the mean intersection of union (MIoU), F1 score and AUC of our improved model are 93.17%, 81.51%, 86.95% and 0.9928 respectively. The F1 Score and AUC of our improved model are 11.3% and 11.4% higher than those of the standard U-net model respectively.
Efficient Distributed Range Query Processing in Apache Spark

Range queries are important in many diverse applications. In its simplest one-dimensional form, a range query is expressed by an interval [a, b] on the real line, whereas the answer consists of all elements ε in [a, b]. In this work, we focus on efficient range query processing techniques in the Apache Spark engine, which is the state-of-the-art solution for big data management and analytics. We aim at developing a Spark-based indexing scheme that supports range queries in such large-scale decentralized environments and scale well w.r. t. the number of nodes and the data items stored. Towards this goal, there have been solutions in the last few years, which however turn out to be inadequate at the envisaged scale, since the classic linear or even the logarithmic complexity (for point queries) is still too expensive, whereas range query processing is even more demanding. In this paper, we go one step further and present a solution with sub-logarithmic complexity. In particular, we present SPIS (SPark-based Interpolation Search), a tree structure that outperforms the existing Spark built-in lookup techniques. We carry out an experimental evaluation by using synthetic data sets. Our experimental results demonstrate the efficiency and scalability of the proposed approach.
Performance of Sentimental Analysis by Studying and Mining Social Media using Parsing Technique

The social media networks have evolved quickly and many people often use these services to communicate with others and categorize themselves by sharing their opinions, views and concepts. It is usually dispensed by winnowing the corresponding or interlinked events mentioned on social media websites like Twitter, Facebook, YouTube and Pinterest etc. A social media analysis has rapt from being a unidirectional to a bidirectional dialogue i.e. between generations to generation. The rudimentary objective behind such analyses is to visualize the extent of criticality with relevancy criticism or appreciation delineate at intervals the comments, tweets or blogs. This analysis shows how individuals assume, assess, talk and opine concerning completely different problems.This paper centers on the pattern location and examine the feelings communicated in a particular sentence, passage or archive. The opinions and sentiments are extracted from the collected data and it is fairly estimated based on the degree of quality. The rule commitment of this paper is to give a blueprint to the individuals who try to use internet based life scratching and examination utilizing distinctive programming apparatuses either in their investigation or business. In addition, the system design to implement the proposed idea has been discussed.
FEM Modeling and experimental testing of notched MEMS specimen

This paper presents the simulation of specifically MEMS specimen in the presence of stress concentration factor due to notches. FEM models are realized to study the effect of notch geometric parameters on the stress concentration factor of the gold specimen subjected to tensile loading. Test structures without notch, and with single notch are modeled and realized Maximum axial stresses produced in the specimens and the corresponding stress concentration factors for the notched specimens are obtained using FEM modeling. Realized test structure has been firstly experimentally tested by using profilometers measurements. A first identification of the numerical FEM model is obtained by comparison of the central deflection of the specimen with the central deflection of the measured notched specimen.
A Capacitor-less LDO with Fast transient response using Push-Pull Buffer

This paper presents a fast transient capacitor-less low-dropout regulator (LDO). A Push-Pull Buffer (PPB) is proposed to drive the pass transistor. The low output resistance of the PPB allows the pole at the gate of the pass transistor to be pushed beyond the unity-gain frequency of the LDO regulation loop. Transient response is improved, the settling time of LDO are $0.2\mu \mathrm{S}\text{@} \mathrm{C}_{\mathrm{L}}=0\mathrm{p}$F and $1.14\mu \mathrm{S}\text{@}\mathrm{C}_{\mathrm{L}}=100\mathrm{p}$F. The input transistors of error amplifier are realized Cascode structure which is used for connecting compensation capacitor Cc. With this structure, RHP zero generated by compensation capacitor is pushed beyond the unity-gain frequency of the LDO regulation loop. And LHP zero generated by the Cc is used to compensate the non-dominate poles. Compensation capacitance is 1pF. The Dynamic Transient Improvement (DTI) circuit is adopted to reduce the ripples of the output voltage of LDO. The proposed LDO can operate from a supply voltage of 2.2V-4V with a minimum dropout voltage of 0. 2V at a maximum 50mA load and quiescent current of 70$\mu$A. Only 4pF capacitor is integrated in the chip, the output of LDO can be connected 0- 100pF off-chip capacitor.