Miracle-dz/UniFAR-FTUs huggingface dataset:
Performance Analysis and Evaluation of Different Data Mining Algorithms used for Cancer Classification

Classification algorithms of data mining have been successfully applied in the recent years to predict cancer based on the gene expression data. Micro-array is a powerful diagnostic tool that can generate handful information of gene expression of all the human genes in a cell at once. Various classification algorithms can be applied on such micro-array data to devise methods that can predict the occurrence of tumor. However, the accuracy of such methods differ according to the classification algorithm used. Identifying the best classification algorithm among all available is a challenging task. In this study, we have made a comprehensive comparative analysis of 14 different classification algorithms and their performance has been evaluated by using 3 different cancer data sets. The results indicate that none of the classifiers outperformed all others in terms of the accuracy when applied on all the 3 data sets. Most of the algorithms performed better as the size of the data set is increased. We recommend the users not to stick to a particular classification method and should evaluate different classification algorithms and select the better algorithm.
The Influences of Employees and Managers on Interorganizational Systems: A Resources-Based Perspective

Previous research has emphasized the importance of employees, managers and human efforts in general in attaining operational and strategic benefits from the use of IT. This paper focuses on how human efforts influence operational and strategic benefits attained from the use of the interorganizational systems (IOSs). A conceptual model is constructed combining transaction-cost economics, resource-based view, operational benefits and strategic benefits. The empirical data is acquired through a mixed-method approach by combining two qualitative case studies and a quantitative field study. The findings indicate that human efforts positively influence two distinctive IOS capabilities: cross-organizational business processes and cross-organizational transfer of information and knowledge. The data also show to some extent support for the hypotheses that crossorganizational business processes positively influence the attainment of operational benefit and crossorganizational knowledge transfer positively influence the attainment of strategic benefits.
Challenges in establishing free space optical communications between flying vehicles

Establishing free space optical links between flying vehicles is a technically tough challenge and needs enhancements both in the technology and its features. For unmanned aerial vehicle (UAV) swarms to evolve to effective usage in civil and military applications, a means of high data rate communications need be established between them. The free space optical links due to their huge bandwidth and license free spectrum provide a viable communications solution between the UAV swarm. The swarm UAV scenario due to the continuous motion and changing relative speeds of all its members provide extremely challenging conditions to maintain a line-of-sight free space optical link and this paper reviews the existing related scientific developments and talks about the challenges of utilizing FSO in the swarm UAV environment. Further, this survey paper discusses the utility and challenges of developing free space optical links between HAPs.
A Rate Code for Sound Azimuth in Monkey Auditory Cortex: Implications for Human Neuroimaging Studies

Is sound location represented in the auditory cortex of humans and monkeys? Human neuroimaging experiments have had only mixed success at demonstrating sound location sensitivity in primary auditory cortex. This is in apparent conflict with studies in monkeys and other animals, in which single-unit recording studies have found stronger evidence for spatial sensitivity. Does this apparent discrepancy reflect a difference between humans and animals, or does it reflect differences in the sensitivity of the methods used for assessing the representation of sound location? The sensitivity of imaging methods such as functional magnetic resonance imaging depends on the following two key aspects of the underlying neuronal population: (1) what kind of spatial sensitivity individual neurons exhibit and (2) whether neurons with similar response preferences are clustered within the brain. To address this question, we conducted a single-unit recording study in monkeys. We investigated the nature of spatial sensitivity in individual auditory cortical neurons to determine whether they have receptive fields (place code) or monotonic (rate code) sensitivity to sound azimuth. Second, we tested how strongly the population of neurons favors contralateral locations. We report here that the majority of neurons show predominantly monotonic azimuthal sensitivity, forming a rate code for sound azimuth, but that at the population level the degree of contralaterality is modest. This suggests that the weakness of the evidence for spatial sensitivity in human neuroimaging studies of auditory cortex may be attributable to limited lateralization at the population level, despite what may be considerable spatial sensitivity in individual neurons.
AER spike-processing filter simulator: Implementation of an AER simulator based on cellular automata

Spike-based systems are neuro-inspired circuits implementations traditionally used for sensory systems or sensor signal processing. Address-Event-Representation (AER) is a neuromorphic communication protocol for transferring asynchronous events between VLSI spike-based chips. These neuro-inspired implementations allow developing complex, multilayer, multichip neuromorphic systems and have been used to design sensor chips, such as retinas and cochlea, processing chips, e.g. filters, and learning chips. Furthermore, Cellular Automata (CA) is a bio-inspired processing model for problem solving. This approach divides the processing synchronous cells which change their states at the same time in order to get the solution. This paper presents a software simulator able to gather several spike-based elements into the same workspace in order to test a CA architecture based on AER before a hardware implementation. Furthermore this simulator produces VHDL for testing the AER-CA into the FPGA of the USB-AER AER-tool.
MiniSAT with Classification-based Preprocessing

We present a classification-based approach to selecting preprocessors of CNF formulas for the complete solver MiniSAT. Three different preprocessors are considered prior to running MiniSAT. To obtain training data for classification, each preprocessor is run, followed by running MiniSAT on its resulting CNF, on instances from the last competition. On each instance, the preprocessor leading to the smallest solving time is considered the “best” and used as the classification for the instance. A decision tree is trained to select the best preprocessor according to features computed from the CNF formulas. The decision tree can therefore be used to predict the best preprocessor for new problem instances.
3D watermarking design evaluation

Due to the inherent combination of image, video, audio and 3D-models in new MPEG standards like MPEG-4, robust 3D-watermarking is becoming more and more popular to ensure data authenticity and integrity. Beside the design of 3D-watermarking schemes, the evaluation is necessary. In the paper we analyze currently proposed 3D watermarking algorithms for weaknesses that may possibly be exploited in watermarking attacks and derive according design principles for improving algorithms. The paper makes contributions in three respects: First we analyze properties with respect to preservation of surface continuity and symmetries. Second, we analyze how algorithms proposed by Ohbuchi et al. and Praun et al. compensate for errors introduced through imperfect mesh resampling. For Ohbuchi et al. algorithm, we explain how spectral decomposition can be applied in directions other than canonical coordinate system axes in order to minimize errors introduced to resampling. With an experimental prototyped algorithm based on spectral decomposition, we demonstrate a significant increase of robustness of features with respect to errors introduced in resampling. Third, we analyzed the general effects caused by polygon simplification on watermarking algorithms, which, from the results from Praun et al. can be considered as a “critical” operation. As a first result of our analysis, we present a modification of Praun et al. watermarking scheme detector trying to improve compensation for these effects.
Synchronizing continuous-time neutrally stable linear systems via partial-state coupling

Synchronization of coupled continuous-time linear systems is studied in a general setting. For identical neutrally-stable linear systems that are detectable from their outputs, it is shown that a linear output feedback law exists under which the coupled systems globally asymptotically synchronize under all fixed (directed) connected network topologies. An algorithm is provided to compute one such feedback law based on individual system parameters. The dual case, where individual systems are neutrally stable and stabilizable from their inputs, is also considered and parallel results are established.
A Nearest Neighbor Classifier Employing Critical Boundary Vectors for Efficient On-Chip Template Reduction

Aiming at efficient data condensation and improving accuracy, this paper presents a hardware-friendly template reduction (TR) method for the nearest neighbor (NN) classifiers by introducing the concept of critical boundary vectors. A hardware system is also implemented to demonstrate the feasibility of using an field-programmable gate array (FPGA) to accelerate the proposed method. Initially, k-means centers are used as substitutes for the entire template set. Then, to enhance the classification performance, critical boundary vectors are selected by a novel learning algorithm, which is completed within a single iteration. Moreover, to remove noisy boundary vectors that can mislead the classification in a generalized manner, a global categorization scheme has been explored and applied to the algorithm. The global characterization automatically categorizes each classification problem and rapidly selects the boundary vectors according to the nature of the problem. Finally, only critical boundary vectors and k-means centers are used as the new template set for classification. Experimental results for 24 data sets show that the proposed algorithm can effectively reduce the number of template vectors for classification with a high learning speed. At the same time, it improves the accuracy by an average of 2.17% compared with the traditional NN classifiers and also shows greater accuracy than seven other TR methods. We have shown the feasibility of using a proof-of-concept FPGA system of 256 64-D vectors to accelerate the proposed method on hardware. At a 50-MHz clock frequency, the proposed system achieves a 3.86 times higher learning speed than on a 3.4-GHz PC, while consuming only 1% of the power of that used by the PC.
Voice of Customer Analysis for Internet Shopping Malls

Advances in Internet and Smartphones are generating huge amount of customers’ opinion (or VOC: voice of customer) in the online world. VOC is very useful for marketing or product design departments to see the customers’ opinion for the products. In this paper, VOC in online review sites has been collected, stored and analyzed in order to find out what kind of factors can affect the customer preference for the electronics products. We have implemented a VOC analysis system that includes ETL (extract, transformation, and loading), natural language processing, positive/negative analysis, and result visualization. Sensitivity adjectives and synonyms are collected and stored as a database for more correct sentiment analysis. Real experiment for VOC data with 39 Giga bytes from well-known review sites in Korea shows that the correctness for the classification of the positive/negative sentences and the causes of the positive/negative are 73% and 70%, respectively. The evaluation has been done based on the manual inspection for the analysis result
Risk management of energy derivatives : hedging and margin requirements

The recent growth of exchanges has generated large trading platforms for investors. The largest of these institutions, the Intercontinental Exchange and the Chicago Mercantile Exchange group are now responsible for clearing trades for the majority of investors worldwide and are perhaps, as large commercial banks are, too big to fail. This has attracted attention from international regulating bodies to impose strict risk management standards on the exchanges to ensure financial stability. In this thesis, we identify first, that an investor in the market is strongly affected by margins set by the exchanges in determining the transaction costs of a trade. We discuss the possibility that a volatile margin movement would introduce further risks for such an investor causing them to raise more capital to cover possible margin calls which can perhaps lead to procyclicality. We follow this work by addressing how margins can be determined in adherence to the new laws. Exchanges are now required to set margins based on the Value-at-Risk, hence we search for the best Value-at-Risk method for margining use. Here, we find that the simple Orthogonal Exponentially Weighted Moving Average method is sufficient in forecasting the Value-at-Risk, which contradicts a fair body of the literature who suggests that complex developments of GARCH are superior. We then offer methods for setting and evaluating margin requirements upon the Value-at-Risk estimates, concentrating on producing stable margin requirements. The automated methods produced in our work outperform all other methods available in the literature. Furthermore, we are the first to provide methods for assessing margin stability. Our work is timely in addressing the current affairs of the world economy and is among the first to tackle the margin stability issue in detail.
Mixed integer programming constrained discrimination model for credit screening

Classification, the development of rules for the allocation of observations to groups, is a fundamental machine learning task. A classic example is an automated system for a lending institution that decides whether to accept or reject a credit application. One might desire a machine that allows the non-classification of certain observations that exhibit attributes of belonging to more than one group. This option would allow inspection by an expert for "difficult" cases, or serve as an indication that more data needs to be collected. Classification with an option to reserve judgment on an observation is known as constrained discrimination. We consider a two-stage model for multi-category constrained discrimination in which limits on misclassification rates of training observations may be pre-specified. The mechanism by which the misclassification limits are satisfied is a rejection option, also known as a reserved judgment group, for observations not demonstrating properties of membership to any of the groups.
A Rule-based Approach for Karmina Generation

We present our work in generating Karmina, an old Malay poetic form for Indonesian language. Karmina is a poem with two lines that consists of a hook ( sampiran ) on the first line and a message on the second line. One of the unique aspects of Karmina is in the absence of discourse relation between its hook and message. We approached the problem by generating the hooks and the messages in separate processes using predefined schemas and a manually built knowledge base. The Karminas were produced by randomly pairing the messages with the hooks, subject to the constraints imposed on the rhymes and on the structure similarity. Syllabifications were performed on the cue words of the hooks and messages to ensure the generated pairs have matching rhymes. We were able to generate a number of positive examples while still leaving room for improvement, particularly in the generation of the messages, which currently are still limited, and in filtering the negative results.
3D shape prediction of a paper model of a two piece brassiere cup for its efficient design

A method to predict the shape of a paper model of a two piece brassiere cup is proposed. A brassiere cup consists of several parts made of cloth and wires. The shape of each part is determined by making a paper model of the cup and checking the 3D shape of the model. For efficient design of parts, it is required to predict the 3D shape of such model with simulation. As a model is made of paper, each part is inextensible and its deformed surface is developable. Then, the potential energy of a part can be represented as a function of the principal curvature and the principal direction along an edge of the part. Minimizing the potential energy under geometric constraints, the stable shape of the cup model is derived. The computed shape of the cup almost coincided with the measured shape of an actual paper model.
USING BRANCH SIGNATURES FOR ACCURATE , LOW-OVERHEAD DYNAMIC DETECTION AND PREDICTION OF PROGRAM PHASES

We introduce a hardware-only program phase detection and prediction architecture, which improves on the existing proposals by forming the execution footprints using simple bit-vectors called “branch signatures” to capture the set of branches touched during an execution interval. Previous work, in contrast, used the number of instructions executed between the branches to form the footprints. Such a modification significantly simplifies the phase detection logic and also affords numerous additional advantages, such as the detection of fewer distinct phases, less frequent phase transitions and higher phase prediction accuracies. We also show, through extensive simulations, that our simplified phase detection logic performs on par with the original proposal on several phase-based optimizations, such as the processor width adaptation and the frequent value locality exploitation. At the same time, our scheme requires only a fraction of the storage needed by the previous scheme to keep the phase-related information.
Residential Energy Storage from Repurposed Electric Vehicle Batteries: Market Overview and Development of a Service-Centered Business Model

Sales figures for electric vehicles still lag behind expectations. Most prominently, limited driving ranges, missing charging stations, and high purchase costs make electric vehicles less attractive than gas-operated vehicles. A huge share of these costs is caused by the electric vehicle battery. Since the batteries' performance degrades over use and time, replacements become necessary to restore the vehicle's driving range, acceleration, and charging speed. Despite the degradation, repurposing used batteries for a further use in less demanding second-life application scenarios seems feasible and desirable. However, neither a market nor business models for further using electric vehicle batteries exist, yet. In this paper, we set out to review existing business models for residential battery energy storage systems and suggest a re-design to open up a market for storage systems that build on used electric vehicle batteries. The re-design is informed by lemon-market theory. To mitigate market failure caused by asymmetric information, the re-designed business model strongly bases on offering value-added services. The services compensate perceived defects of used batteries and signal their quality. With these results, we provide much-needed conceptual insights on how business models for repurposed electric vehicle batteries could be established.
Composite Quantization for Approximate Nearest Neighbor Search

This paper presents a novel compact coding approach, composite quantization, for approximate nearest neighbor search. The idea is to use the composition of several elements selected from the dictionaries to accurately approximate a vector and to represent the vector by a short code composed of the indices of the selected elements. To efficiently compute the approximate distance of a query to a database vector using the short code, we introduce an extra constraint, constant inter-dictionary-element-product, resulting in that approximating the distance only using the distance of the query to each selected element is enough for nearest neighbor search. Experimental comparisonwith state-of-the-art algorithms over several benchmark datasets demonstrates the efficacy of the proposed approach.
Integrating custom index extensions into virtuoso RDF store for e-commerce applications

Triple stores are the backbone of the evolution of the Linked Data Web. However, even standard queries on many attributes can result in high query response times using available triple stores. For use in commercial search applications, particularly for web scale applications, short and predictable query response times are a strict necessity in order to achieve industrial applicability. In this paper, a custom index extension for the Virtuoso triple store is used to improve the runtime of typical queries. The index extension is connected to a lightweight bitset index that covers the properties in question. A case study with real world data in the e-commerce domain shows that more comprehensive queries even on highly materialized data result in consistently improved query response times with a predictable upper bound that meets industrial requirements. Hence, to the best of our knowledge, industrial applicability is achieved for the first time considering the requirements of the e-commerce industry.
Wireless secret key generation exploiting reactance-domain scalar response of multipath fading channels

We describe a secure communication scheme that uses the random fluctuation of the natural environment of communication channels. Only the transmitter and the receiver share the communication channel characteristics. From reciprocity between a transmitter and a receiver, it is possible for them to share one-time information of their fluctuating channel. This can provide a secret key agreement scheme without key management and key distribution processes. In this paper, we propose a new secret key generation and agreement scheme that uses the fluctuation of channel characteristics with an electronically steerable parasitic array radiator (ESPAR) antenna. This antenna, which has been proposed and prototyped, is a smart antenna designed for consumers. Using the beam-forming technique of the ESPAR antenna, we can increase the fluctuation of the channel characteristics. From experimental results, we conclude that the proposed scheme has the ability to generate secret keys from the received signal strength indicator (RSSI) profile with sufficient independence.
HoloFlex: A Flexible Light-Field Smartphone with a Microlens Array and a P-OLED Touchscreen

We present HoloFlex, a 3D flexible smartphone featuring a light-field display consisting of a high-resolution P-OLED display and an array of 16,640 microlenses. HoloFlex allows mobile users to interact with 3D images featuring natural visual cues such as motion parallax and stereoscopy without glasses or head tracking. Its flexibility allows the use of bend input for interacting with 3D objects along the z axis. Images are rendered into 12-pixel wide circular blocks-pinhole views of the 3D scene-which enable ~80 unique viewports at an effective resolution of 160 × 104. The microlens array distributes each pixel from the display in a direction that preserves the angular information of light rays in the 3D scene. We present a preliminary study evaluating the effect of bend input vs. a vertical touch screen slider on 3D docking performance. Results indicate that bend input significantly improves movement time in this task. We also present 3D applications including a 3D editor, a 3D Angry Birds game and a 3D teleconferencing system that utilize bend input.