Miracle-dz/UniFAR-FTUs huggingface dataset:
Assignment rules in robotic mobile fulfilment systems for online retailers

Abstract We study robotic mobile fulfilment systems for online retailers, where products are stored in movable shelves and robots transport shelves. While previous studies assume random assignment rule of workstations to robots, we propose an assignment rule based on handling speeds of workstations and design a neighbourhood search algorithm to find a near optimal assignment rule. We build semi-open queueing networks and use a two-phase approximate approach for performance estimation. We first replace workstation service processes by a composite service node and then solve the model by the matrix-geometric method. Simulations are used to validate the analytical models. Numerical experiments are conducted to compare random, handling-speeds-based, near optimal and optimal assignment rules, in terms of retrieval throughput time. The results show that the random assignment rule is not a good choice, the handling-speeds-based assignment rule significantly outperforms the random assignment rule when the workers have large handling time difference, and the neighbourhood search approach can provide an assignment rule that is very close to the optimal one, using a much shorter time. Moreover, we design the shelf blocks under the examined assignment rules, and find that the optimal width of shelf block decreases with the width to length ratio.
Integrated Hybrid Segmentation to Overcome Problems in Brain Tumor Segmentation

Background/Objective: To improve the performance of the segmentation of Brain tumor images by introducing integrated hybrid segmentation approach. Methods/Statistical Analysis: An innovative integrated approach combining a set of features of some efficient algorithms is proposed in this paper. The proposed algorithm known as integrated hybrid segmentation approach uses the features that classify the MRI images based on the local independent projections. The hybrid PAM and enhanced possibilistic fuzzy C-means approach is used in the partition and determination of the cluster centers. Findings: The problem of false segmentation and segmentation with low accuracy that prevails in the brain image segmentation can be overcome by using this approach. The classified images are used as training data using which different dictionaries are constructed with all classes. The testing samples are projected to the dictionaries and reconstructed using local anchor embedding approach. Thus the proposed integrated algorithm improves the segmentation process of brain tumor images considerably. Applications/Improvements: Improved segmentation performance is required for accurate detection of brain tumor. The integrated hybrid segmentation approach has better segmentation accuracy than other segmentation approaches with 6% improvement which is very significant.
Statistical Analysis and Re-generation of the Radiated Disturbances from Multiple Noise Sources

A re-generation method of radiated disturbances from multiple noise sources using statistical approach is suggested. The radiated disturbances from multiple noise sources consisting of an electric board and a Plasma Display Panel (PDP) TV are investigated. The frequency spectrum and the Amplitude Probability Distribution (APD) of radiated noises are measured. The radiated magnetic field from each noise source is measured below 30MHz, and then APD measurement is progressed at the frequencies where the noise sources radiate high disturbances in common. A sequence of noise pulses is observed from PDP TV, and the behavior of the APD of the electric board noise is similar to the Gaussian noise. The parameters of the disturbances are extracted using the Middleton’s class A noise model and approximate empirical method. From the extracted parameters, random noise data is re-generated and radiated using a signal generator and antenna. APD Comparison of the measured multiple noises and re-generated noises is performed. The results show that the re-generation noises are well matched to measured noises. The re-generated noise can be applied to the performance evaluation of communication systems against non-Gaussian multiple noises circumstances. Keywords-multiple noise sources; radiated disturbance; amplitude probability distributio;, electric boar; plasma display panel.
Efficient Quadratic Placement of VLSI Circuits

This thesis describes all steps to place the modules (e.g., standard cell or macro cells) of an integrated circuit on a given chip area. During global placement, a novel net model is used to express the linear netlength accurately in the quadratic objective function. In addition, two forces are integrated in the objective function such that the convergence of the global placement algorithm is assured. To optimize the routability of the circuit, a new approach for congestion estimation is presented and integrated in the placement algorithm. During legalization, the remaining module overlap is removed. For this purpose, two new approaches are described, and both minimize the quadratic movement of the modules. The first approach is suitable for standard cell circuits and the second one is suitable for macro cell circuits.
Modeling Social Perception of Faces

The face is our primary source of visual information for identifying people and reading their emotional and mental states. With the exception of prosopagnosics (who are unable to recognize faces) and those suffering from such disorders of social cognition as autism, people are extremely adept at these two tasks. However, our cognitive powers in this regard come at the price of reading too much into the human face. The face is often treated as a window into a person's true nature. Given the agreement in social perception of faces, this paper discusses that it should be possible to model this perception.
Learning “How” Versus Learning “When”: Improving Transfer of Problem-Solving Principles

How can problem solving be improved in domains where similar principles are learned? A series of three experiments based on Ross (1987) examined how instructing learners about when to apply problem-solving principles may later improve performance. In Experiment 1, subjects studied a similar pair (combinations and permutations) or a distinct pair (combinations and conditional probability) of probability principles. Haft of the subjects received information on when to apply the principles (applicability-instructions condition), whereas other subjects received instructions that reviewed how to solve problems using the principles (procedural-review condition). Subjects who received applicability instructions made fewer confusion errors than subjects who received procedural-review instructions when learning the similar problem pair. However, this instructional manipulation had no effect when subjects were learning the distinct problem pair. Because applicability instructions affect confusion errors but not ove.
A Novel DiDrip Protocol with Dissemination-Privilege Pair Authentication

It is an attempt to enhance the DiDrip protocol. There are some efficiency problems caused by the generation, transmission, and verification of certificates. First, it is not efficient in communication, as the certificate has to be transmitted along with the advertisement packet across every hop as the message propagates in the WSN. A large per-message overhead will result in more energy consumption on each sensor node. Second, to authenticate each advertisement packet, it always takes two expensive signature verification operations because the certificate should always be authenticated first. Once a new user joins the network after the network deployment, the network owner can notify the sensor nodes of the user’s public key/dissemination privilege through using the private key of itself. To enhance secure and distributed data discovery and dissemination protocol (DiDrip), we have planned to use Hop by Hop Message authentication scheme for ensuring data confidentiality.
The Influence of Computer-Mediated Communication on Students’ Writing Ability

There are many ways of teaching writing that can be applied by lecturers to improve the students writing’ ability. Some of them try to improve the students’ writing ability by improving sub-skills of writing: sentence building, developing ideas, organizing ideas, and etc., while others try to use various kinds of learning tools during their teaching and learning process. However, the use of computer and internet cannot be ignored in this modern era. This study aimed to show whether there is a significant influence of Computer-Mediated Communication (CMC) application on students’ writing ability and to know about the students' perceptions on CMC application in Writing classes. One group pretest-posttest experimental design was conducted at English Study Program, Faculty of Teachers Training and Education– Riau University on odd semester of academic year 2015-2016. Cluster random sampling technique was used to select one class of students to become the sample. These students, then, was given treatment by applying CMC in teaching Writing. The instrument used were a writing test and a questionnaire. The obtained data analyzed by using SPSS version 21 for Windows. The results of the data analysis illustrates that there is significant influence of CMC application on the students’ writing ability. Students' perceptions on CMC application in the learning activities was also good.
Elasticity in the Cloud

Real world workloads are often dynamic. This makes the static scaling of resourcesfatally result in either the waste of resources, if it is based on the estimatedworst case scenario, or the degradation of performance if it is based on the averageworkload. Thanks to the cloud computing model, resources can be provisioned ondemand and scaling can be adapted to the variations of the workload thus achievingelasticity. However, after exploring the existing works, we find that most elasticityframeworks are too generic and fail to meet the specific needs of particularapplications. In this work, we use autonomic loops along with various elasticitytechniques in order to render different types of applications elastic, namelya consolidation service, message-oriented middleware and a stream processingplatform. These elastic solutions have been implemented based on open-sourceapplications and their evaluation shows that they enable resources’ economy withminimal overhead.
Forest Fire Danger Assessment Using SPMD-Model of Computation for Massive Parallel System

A new approach to forecast forest fire danger using parallel computing operations in the computing systems with parallel architecture is the subject of this article. The parallel realization of the deterministic-probabilistic approach to estimate forest fire danger using the SPMD calculation model is presented. The parallel program is implemented using C programming language of high level with the Message Passing Interface (MPI). The computing experiments have been carried out by the Skif Cyberia multiprocessor cluster. The information on efficiency estimation and calculation acceleration is presented for this approach. The approach has been tested in the territory of the Timiryazevskiy forestry in Tomsk region (Russia). The typical results of forest fire danger estimation in the controlled forested territory are presented.
A framework for detection of malicious software in Android handheld systems using Machine Learning techniques.

Nowadays the Android platform is the fastest growing handheld operating system. As such, it has become the most coveted and viable target of malicious applications. The present study aims at designing and developing new approaches to detect malicious applications in Android-based devices. More precisely, MaLDroide (Machine Learning–based Detector for Android malware), a framework for detection of Android malware based on Machine Learning techniques, is introduced here. It is devised to identify malicious applications. To start with, features of real–world known-benign and known–malicious applications are extracted, gathering 299 features grouped into 8 categories: stats, requested permissions, used permission, API calls, Intents, risks, system calls, and content access. MaLDroide uses static analysis in order to automatically extract these features. As an essential part of the work, a mechanism for dimensionality reduction, using Principal Component Analysis (PCA), was applied, the original 299 features are reduced to 27, retaining 90% of the variance, and dramatically reducing time and space consumption. Once determined the appropriate data set, a training process of the seven most widely used Machine Learning algorithms (Naive Bayes, Decision Tree, Random Forest, k-Nearest Neighbor, Support Vector Machine, Multi-Layer Perceptron, and AdaBoost) is undertaken. After an evaluation process with 436 normal applications and 2295 malware samples, and on the basis of the evaluation results, one can conclude that Support Vector Machine classifier produces the most accurate predictions, and identifies malware with an accuracy of 99.8%. The evaluation also shows that our framework constitutes a valuable tool which is effective in detecting malware for Android devices.
A Learning Automata Approach for Input-rate Control in Composable Conveyor Systems

Simple and effective control techniques are necessary to improve the Quality of Service (QoS) in a variety of composable and reconfigurable networked systems. Traditional approaches to improve QoS are model-based and rely on representations of the state of the system, at multiple levels. This thesis presents a Learning Automata based approach to improve QoS that does not need to explicitly represent or estimate system state. A Learning Automaton (LA) is a model for adaptive decision-making that uses only stochastic feedback, also called reinforcement, from the environment and does not rely on detailed models or estimation of parameters. It learns to choose the optimal actions from a finite set of actions using such interactions with its environment. This investigation focused on a class of composable conveyor systems that represent a confluence of embedded and real-time systems, wireless communication, sensing and actuating devices, and networking technologies. In such systems, inputrate, i.e., the rate at which parts are injected into the system affects QoS. The inputrate controller presented in this thesis learns the “correct” rate of injection at an input based on noisy feedback on the latency and throughput of parts that moved through iii the system recently. When it is important to deliver each part at its output before its deadline is elapsed, an admission controller must be used to determine whether the part can be so delivered or not. Preliminary results on value of a probabilistic admission controller are also presented in this thesis. Simulation results confirm the performance of these controllers. These results motivate several investigations in the future to design better admission controllers, route controllers and congestion controllers for composable and networked systems that are based on simple adaptive mechanisms.
Low power programmable front-end for a multichannel neural recording interface

In this paper a programmable gain preamplification front-end for a fully implantable multichannel data acquisition system (IMDAS) that is dedicated for chronic neural signal recording is proposed. This application calls for very low power and low voltage circuit techniques. To satisfy these constraints, the preamplifier is designed in 0.18 /spl mu/m CMOS technology and all employed transistors operate in weak inversion. To maximize the dynamic range of the recorded signals, the preamplifier's gain is set by a 4-bit digital-to-analog converter (DAC). This DAC is used to tune the bias currents of a variable transconductor cell in order to vary its DC gain. The simulation of the whole proposed module gives a maximum power consumption of 530 nW at a supply voltage of 0.9 V. The circuit provides a maximum gain of 47 dB, has a cutoff frequency of 2.65 KHz and presents an input-referred noise of 7.65 /spl mu/Vrms which is sufficiently low to meet the required precision. In addition, the phase margin is higher than 50/spl deg/ on the entire gain range.
Contextual typesetting of mathematical symbols taking care of optical scaling

Typesetting of mathematical formulae has conflicting requirements, on the one hand, optical scaling is a need for large symbols, in the other hand, large symbols are made of composite items that are neither easily nor nicely put together. In this paper it is shown that such large symbols should be computed at print time so that they reach the quality of metal typesetting. An implementation of such dynamic fonts is in progress in the Grif editor.
Constrained Partial Parsing Based Dependency Tree Projection for Tree-to-Tree Machine Translation

Ideally, tree-to-tree machine translation (MT) that utilizes syntactic parse trees on both source and target sides could preserve non-local structure, and thus generate fluent and accurate translations. In practice, however, firstly, high quality parsers for both source and target languages are difficult to obtain; secondly, even if we have high quality parsers on both sides, they still can be non-isomorphic because of the annotation criterion difference between the two languages. The lack of isomorphism between the parse trees makes it difficult to extract translation rules. This extremely limits the performance of tree-to-tree MT. In this article, we present an approach that projects dependency parse trees from the language side that has a high quality parser, to the side that has a low quality parser, to improve the isomorphism of the parse trees. We first project a part of the dependencies with high confidence to make a partial parse tree, and then complement the remaining dependencies with partial parsing constrained by the already projected dependencies. Experiments conducted on the Japanese-Chinese and English-Chinese language pairs show that our proposed method significantly improves the performance on both the two language pairs.
Word frequency in context: Alternative architectures for examining related words, register variation, and historical change

The use of relational databases that are composed of the frequency of n-gram in a given corpus allows users to quickly and easily examine word frequency. Perhaps the first large corpus to use such an approach was the 100 million word Corpus del Español, which was created in 2002 (www.corpusdelespanol.org/). This was followed by two BNC-based 100 million word corpora that were modelled on the same architecture: Phrases in English (pie.usna.edu) and Variation in English Words and Phrases (VIEW; view.byu.edu), as well as a 40 million word Corpus of Historical English (view.byu.edu/che) The relational database/n-grams architecture allows simple word frequency queries such as the following (all of which can be carried out on a 100 million word corpus in 1-2 seconds): • Overall frequency of a given word, set of words, phrase, or substring in the corpus • 'Slot-based' queries; e.g. the most common nouns one 'slot' after mysterious, or z-score ranked words immediately preceding chair • Wide-range collocates; e.g. the most common nouns within a ten word window (left or right) of string or broken In addition, however, the architecture that we have used for VIEW and the Corpus of Historical English allows several other types of queries that cannot be carried out directly with competing architectures (e.g. SARA/XARA, the IMS Corpus Workbench, or the Phrases in English architecture), including the following: • Comparison of frequency with related words; e.g. nouns occurring immediately after utter but not after complete or sheer, or adjectives within ten words of woman but not man • One simple query to find the frequency of words in separate databases, such as user-defined, customized lists (clothing, emotions, technology terms, etc) or synsets from WordNet • Register variation; e.g. all verbs or all words ending in *ble or all three-word lexical bundles that are more common in academic texts than in fiction, or in legal or medical texts • Historical variation; e.g. words, phrases, or collocates of a given word or part of speech, which are more common in the 1900s than in the 1800s Finally, even within the relational database/n-grams approach, there are competing architectures that favour certain types of queries over others, and we will briefly consider some of these issues.
The energy efficiency of 8-bit low-power microcontrollers

We have measured the energy cost of processing, sleeping, non-volatile memory writes and ADC measurements of six 8-bit microprocessors from three manufacturers. These measurements compare the chips directly to one another and reveal ideal operating points which can be used to reduce energy consumption. I. INTRODUCTION Moving to a new family of microprocessor (MCU) can be a daunting task for an electronic engineer. Making com- parisons between families of processors is difficult because of the differences in architecture and the types of available data. Therefore most engineers tend to favour one family of processor simply because it's what they know or understand. We aim to compare a small set of 8-bit microprocessors based on energy consumption and computational efficiency. The MCUs that will be investigated are shown in Table I. This investigation has been carried out in order to estimate the
Improving Relay Matrices for MIMO Multi-Relay Communication Using Gradient Projection

In this paper, we design the optimal relay matricesfor multiple-input multiple-output (MIMO) relay communicationsystems with parallel relay nodes using the projected gradient(PG) approach. We show that the optimal relay amplifyingmatrices have a beamforming structure. Using the optimalstructure, the relay power loading algorithm is developed tominimize the mean-squared error (MSE) of the signal waveformestimation at the destination node. Simulation result demonstratethe effectiveness of the proposed relay amplifying matrix withmultiple parallel relay nodes using the PG approach in the systembit-error-rate performance. Index Terms—MIMO relay, parallel relay network, beamforming,non-regenerative relay, projected gradient.
An optimization method for speech enhancement based on deep neural network

Now, this document puts forward a deep neural network (DNN) model with more credible data set and more robust structure. First, we take two regularization skills, dropout and sparsity constraint to strengthen the generalization ability of the model. In this way, not only the model is able to reach the consistency between the pre-training model and the fine-tuning model, but also it reduce resource consumption. Then network compression by weights sharing and quantization is allowed to reduce storage cost. In the end, we evaluate the quality of the reconstructed speech according to different criterion. The result proofs that the improved framework has good performance on speech enhancement and meets the requirement of speech processing.
An Experimental Constructional Database : The MorTAL Project

In this paper, we will present the prerequisites for the semi-automatic generation of a constructional database created for the MorTAL project (“MORphology for Automatic Language Processing”). As we will demonstrate, the construction of this database depends, for the moment, on the synergy of two complementary approaches : (i) a linguistic approach, (ii) a statistic approach. Our presentation will be made in three parts : first, we will briefly present the project which constitutes the general context for our database, then our database in particular ; we will then give a detailed explanation of the approaches chosen to build our lexicon ; finally, we will compare the results obtained by each approach.