Miracle-dz/UniFAR-FTUs huggingface dataset:
Design of Automatic Cooling Power Hacksaw Machine for Multipurpose Applications

This study involves a comparative analysis of a designed automatic cooling power hacksaw machine and manual cooling power hacksaw machine in a local sawmill where coolant is applied manually by the operator. The automatic cooling power hacksaw machine took an average time of 40 s to cut an average mass of 5.9 kg with average Specific Mechanical Energy (SME) of 29 kj/kg and average cutting speed of 270 rpm. However, the manual cooling hacksaw machine took an average time of 53 s to cut the same average mass of 5.9 kg with average SME of 42 kj/kg and average cutting speed of 269 rpm. The basic idea behind SME was to determine the energy going into the cutting operation process per unit mass of timber in form of work from the motor. From the above stated results, the automatic cooling hacksaw machine designed in this study took less time, less SME and slightly higher cutting speed to cut the same quantity of timber than the manual cooling hacksaw machine. Compared to the manual cooling power hacksaw, the automatic cooling power hacksaw machine is obviously more efficient in terms of time and energy savings.
Towards Scalable Analytics with Inference-Enabled Solid-State Drives

In this paper, we propose a novel storage architecture, called an Inference-Enabled SSD (IESSD), which employs FPGA-based DNN inference accelerators inside an SSD. IESSD is capable of performing DNN operations inside an SSD, avoiding frequent data movements between application servers and data storage. This boosts up analytics performance of DNN applications. Moreover, by placing accelerators near data within an SSD, IESSD delivers scalable analytics performance which improves with the amount of data to analyze. To evaluate its effectiveness, we implement an FPGA-based proof-of-concept prototype of IESSD and carry out a case study with an image tagging (classification) application. Our preliminary results show that IESSD exhibits 1.81× better performance, achieving 5.31× lower power consumption, over a conventional system with GPU accelerators.
Filtro de Información Unscented en Arquitectura Distribuida aplicada a la destilación Unscented Information filter in Distributed Architecture applied to distillation

Este articulo presenta la aplicacion del filtro de Informacion Unscented para la estimacion de estado estimacion de estado de una columna de destilacion por lotes a traves de la fusion de multiples filtros individuales, bajo un esquema distribuido. Se propone el uso de un esquema federado usando un algoritmo de Informacion Unscented Adaptativo. La propuesta de filtro consiste en utilizar dos estrategias: a) Filtros locales de Informacion Adaptativos, cada filtro local de informacion sera robusto a las variaciones de las senales de medicion, por medio de la adaptacion de la covarianza del ruido. b) Un filtro maestro que trabajara bajo una estructura de Informacion Federada con factor de distribucion de informacion adaptativo. El filtro es comparado con las tradicionales soluciones centralizadas y con un esquema de filtro distribuido con deteccion de fallas. Los resultados muestran que el filtro de Informacion Unscented Adaptativo tiene el mismo rendimiento que el filtro distribuido con deteccion de fallas, sin presentar los inconvenientes de deteccion erronea de fallas en el sistema. Palabras clave: Filtro de Informacion Unscented adaptativo, tolerancia a fallas, destilacion por lote. Abstract This article summits the state estimation of a batch destillation column through fusion information from multiples indi-vidual filters, under a Federated architecture using an Adaptive Unscented Information algorithm. The proposed of the filter consist in the use of two strategies: a) Locals adaptives information filters, where each local information filter will be robust to the variations of the measurement signals, by adjusting the noise covariance. b) A master filter that will work under a structure of information federated with adaptive information sharing factorThe filter is compared with traditional centralized solution and a distributed mode filter fault detection. The results show that the Adaptive Unscented Information filter has the same performance as the distributed filter with fault detection, without presenting the drawbacks of erroneous detection of faults in the system. Keywords: Adaptive Kalman filter, Sensor fault tolerance, Batch distillation.
A Feature Based Data Structure for Efficient Index

Data collected from our daily lives by mobile electronic devices always contains errors. This paper proposed a feature based data structure to organize the dictionary for correcting the errors efficiently. The key points in the method includes explicit splitting the dictionary by high frequency used elements, implicit splitting further by clustering, and a hierarchical match procedure. The numerical results indicated the proposed methods can index the input effectively while reducing the consumption in both time and RAM.
Efficient Verification of Concurrent Systems Using Synchronisation Analysis and SAT/SMT Solving

This article investigates how the use of approximations can make the formal verification of concurrent systems scalable. We propose the idea of synchronisation analysis to automatically capture global invariants and approximate reachability. We calculate invariants on how components participate on global system synchronisations and use a notion of consistency between these invariants to establish whether components can effectively communicate to reach some system state. Our synchronisation-analysis techniques try to show either that a system state is unreachable by demonstrating that components cannot agree on the order they participate in system rules or that a system state is unreachable by demonstrating components cannot agree on the number of times they participate on system rules. These fully automatic techniques are applied to check deadlock and local-deadlock freedom in the PairStatic framework. It extends Pair (a recent framework where we use pure pairwise analysis of components and SAT checkers to check deadlock and local-deadlock freedom) with techniques to carry out synchronisation analysis. So, not only can it compute the same local invariants that Pair does, it can leverage global invariants found by synchronisation analysis, thereby improving the reachability approximation and tightening our verifications. We implement PairStatic in our DeadlOx tool using SAT/SMT and demonstrate the improvements they create in checking (local) deadlock freedom.
UNDERSTANDING GROVER'S SEARCH ALGORITHM THROUGH A SIMPLE CASE OF STUDY

Quantum computers are emerging as one of the most novels technologies nowadays. These computers work through quantum circuits which, unlike classic computer circuits, follow the set of rules determined by quantum mechanics. However, those rules are counterintuitive, and it is necessary to define appropriate techniques to explain them to students. This work aims to improve the existing approaches to explain Grover’s search algorithm, which is known for demonstrating that quantum computers can outperform the traditional ones. We propose a new methodology to teach it that avoids complicated descriptions and uses a simple application example, namely, the classical division. In this context, the division is seen as the search of a number X that is equal to the dividend when multiplied by the divisor. We develop a complete explanation of the algorithm for this example. It covers the beginning, evolution, and finalization of the method conceptually and physically while also exposing its pros and cons. With this example, it is not necessary knowledge about quantum mechanics, nor neither geometry, only basic knowledge about quantum computing is necessary (which is also introduced in this work). Although there are better ways to solve divisions, we believe it is a perfect example to explain Grover’s algorithm. The objective of this paper is that Computer Engineering students understand the behavior and the bases of Quantum Computation and Grover's algorithm in particular. Therefore, the proposed methodology has been designed for being included in current degrees in Computer Engineering, which do not generally cover this topic despite its future potential.
Graph-based Rumour Detection for Social Media

Social platforms became a major source of rumours. While rumours can have severe real-world implications, their detection is notoriously hard: Content on social platforms is short and lacks semantics; it spreads quickly through a dynamically evolving network; and without considering the context of content, it may be impossible to arrive at a truthful interpretation. Traditional approaches to rumour detection, however, exploit solely a single content modality, e.g., social media posts, which limits their detection accuracy. In this paper, we cope with the aforementioned challenges by means of a multi-modal approach to rumour detection that identifies anomalies in both, the entities (e.g., users, posts, and hashtags) of a social platform and their relations. Based on local anomalies, we show how to detect rumours at the network level, following a graph-based scan approach.
Knee Exoskeleton Assistive Torque Control Based on Real-Time Gait Event Detection

In this paper, an assistive torque control strategy is proposed for the control of a bionic knee exoskeleton based on real-time gait event detection. Real-time gait event detection is executed with an inertial measurement unit based on an onboard trained model. Two models are trained based on the collected data from the knee exoskeleton at normal walking speed under zero torque and assistive torque control modes, respectively. We conduct two experiments: 1) real-time detection experiments under zero torque control at different speeds and 2) real-time detection and assistive torque control experiments based on real-time detection results at different speeds. Five able-bodied subjects participated in the experiments. The recognition accuracy and assistive torque performances based on real-time detection are evaluated. The experimental results show that all the critical gait events are detected rightly and the delay of gait event detection does not lead to damage to the overall system of knee exoskeleton. In addition, in real-time detection and assistive torque control experiments, parameters of the controller are refreshed for the current cycle based on the previous gait cycle, which is reasonable according to the detection result. The tracking performance of the torque could meet the requirement. This paper provides a feasible and applicable method for knee exoskeleton control based on real-time gait event detection.
Hand Gesture Recognition Enhancement Based on Spatial Fuzzy Matching in Leap Motion

Gesture recognition is an important human–computer interaction interface. This article introduces a novel hand gesture recognition system based on Leap Motion gen.2. In this system, a spatial fuzzy matching (SFM) algorithm is first presented by matching and fusing spatial information to construct a fused gesture dataset. For dynamic hand recognition, an initial frame correction strategy based on SFM is proposed to fast initialize the trajectory of test gesture with respect to the gesture dataset. A notable feature of this system is that it can run on ordinary laptops due to the small size of the fused dataset, which accelerates the calculation of recognition rate. Experimental results show that the system recognizes static hand gestures at recognition rates of 94%–100% and over 90% of dynamic gestures using our collected dataset. This can greatly enhance the usability of Leap Motion.
Sorting of Fully Homomorphic Encrypted Cloud Data: Can Partitioning be Effective?

The challenge of maintaining confidentiality of stored data in cloud is of utmost importance to realize the potential of cloud computing as an emerging storage solution service. Storing data in encrypted form may solve the problem, but exposes data to an adversary for each required computation. This repeated encryption decryption also diminishes the essence of cloud for storing encrypted database and huge computation power of cloud remains unused. Fully homomorphic encryption (FHE) is an effective scheme to support arbitrary operations directly on encrypted data, but has serious performance issues. In this paper, we have considered sorting on encrypted data, which is a frequently required database operation. We have investigated the feasibility of performing comparison as well as partition based sort on CPA resistant FHE data and highlight an important observation that time requirement of partition based sort on FHE data is no better than comparison based sort owing to the underlying security of the cryptosystem. We identify the recrypt operation, which is the denoising step of FHE as the main reason of costly timing requirement of such operations. We propose a FHE specific two stage sorting technique termed as <inline-formula><tex-math notation="LaTeX">$\sf {Lazy sort}$</tex-math><alternatives><mml:math><mml:mrow><mml:mi mathvariant="sans-serif">Lazy</mml:mi><mml:mi mathvariant="sans-serif">sort</mml:mi></mml:mrow></mml:math><inline-graphic xlink:href="chatterjee-ieq1-2711018.gif"/></alternatives></inline-formula> with reduced recrypt operation, which proves to be better in terms of performance on FHE data in comparison to partition as well as comparison sort. Finally, we provide some multi-core implementation results to show that with proper implementation tricks performance of FHE computations can be improved further.
THE INFLUENCE OF BLOGGERS ON CONSUMER PURCHASE

This paper evaluates the impact of online blogging on buying intentions of customers. It begins by offering an overview of digital marketing. Next, it analyses the process of e-commerce and its relevance to modern business. Then there is an overview of digital marketing and the manner in which it has affected contemporary business. The paper then offers an insight into the construct of online blogging and how it is used by contemporary organizations. The paper also examines the manner in which blogs are being used for search engine optimization and content marketing to influence the buying behavior of consumers. Finally, the paper concludes by identifying a literature gap and the need to conduct further research work in this area.
On Boosting: Theory and Applications

We provide an overview of two commonly used boosting methodologies. We start with the description of different implementations as well as the statistical theory behind selected algorithms which are widely used by the machine learning community, then we discuss a case study focusing on the prediction of car insurance claims in a fixed future time interval. The results of the case study show that, overall, XGBoost performs better than AdaBoost and it shows best performance when shallow trees, moderate shrinking, the number of iterations increased with respect to default as well as subsampling of both features and training data points are considered.
Evolutionary Synthesis of Sensing Controllers for Voxel-based Soft Robots

Soft robots allow for interesting morphological and behavioral designs because they exhibit more degrees of freedom than robots composed of rigid parts. In particular, voxel-based soft robots (VSRs)—aggregations of elastic cubic building blocks—have attracted the interest of Robotics and Artificial Life researchers. VSRs can be controlled by changing the volume of individual blocks: simple, yet effective controllers that do not exploit the feedback of the environment, have been automatically designed by means of Evolutionary Algorithms (EAs). In this work we explore the possibility of evolving sensing controllers in the form of artificial neural networks: we hence allow the robot to sense the environment in which it moves. Although the search space for a sensing controller is larger than its non-sensing counterpart, we show that effective sensing controllers can be evolved which realize interesting locomotion behaviors. We also experimentally investigate the impact of the VSR morphology on the effectiveness of the search and verify that the sensing controllers are indeed able to exploit their sensing ability for better solving the locomotion task.
Cerrahın anlık yönlendirilebildiği robot yardımlı endoskop kontrol sistemi mimarisi - NeuRoboScope

Mehmet İsmet Can Dede1, Gökhan Kiper1, Tolga Ayav2, Enver Tatlıcıoğlu3, Barbaros Özdemirel3, Omar Maaroof1, Gizem Ateş1 1Makine / 2Bilgisayar / 3Elektrik-Elektronik Mühendisliği Bölümü, İYTE İzmir, Türkiye candede, gokhankiper, tolgaayav, envertatlicioglu, barbarosozdemirel, omarmaaroof, gizemates@iyte.edu. tr Mustafa Berker, İlkay Işıkay, Şahin Hanalioğlu Beyin ve Sinir Cerrahisi Ana Bilim Dalı Hacettepe Üniversitesi Ankara, Türkiye mberker@hacettepe.edu. tr, ilkayisikay,sahinhanalioglu@gmail.com
Kombinasi Mode Cipher Block Chaining Dengan Algoritma Triangle Chain Cipher Pada Penyandian Login Website

Generally, the database access of a website lies in the user login. When the login data is not accompanied by security techniques, it is very easily accessible by other parties. One effort that can be done to solve the problem is to encode the login data of website users based on cryptographic technique algorithm. Triangle Chain Cipher (TCC) is one of the classic cryptographic algorithms that encode data doubly and generate keys randomly along the plain. The process of encryption and decryption are interdependent to be one of the advantages of this algorithm. This algorithm will be more effective when combined with the Cipher Block Chaining (CBC) operation mode that is widely used in modern cryptographic algorithms today. The feedback mechanism between the blocks in this mode of operation is the operating advantage. This study describes how to combine the mode of operation of block chaining cipher with triangle chain cipher algorithm so that cipher data login website generated more random and difficult to find its original meaning.
SYNTACTIC COMPLEXITY IN THE READING MATERIALS OF ENGLISH FOR ACADEMIC PURPOSES LEVELS 1 – 3

In Indonesia, English is still considered as a foreign language and has become a crucial subject of study especially in the university level. For this reason, English for Academic Purposes has been conducted in the first year of college level for many years. Unfortunately, although many Asian countries including Indonesia have run the EAP course, the output is that there are still many Indonesian students who do not meet the vocabulary size and syntactic complexity that are expected while their learning process in the university. This results lower grades that they have in their assignments. Therefore, the recent study is aimed at evaluating the reading materials of EAP, especially in measuring the syntactic complexity containing in the texts as it is strongly believed in English learning that a good language output comes from a good language input. The data is taken from the collections of reading materials taken from EAP course Level 1, Level 2, and Level 3 which are compulsory subjects for students at Sampoerna University in their first 2 years of study. The data then is processed using the Syntactic Complexity Analyzer (Ai & Lu, 2013). The findings showed that the reading materials of EAP course Level 3 text is mainly suggested to be reviewed and revised in order to fulfill the five categories of syntactic complexity, i.e. the length of production unit, the sentence complexity, the subordination, the coordination, and the particular structure. DOI: doi.org/10.24071/llt.2017. 200204
Time Series Optimization on Data Mining

Forecasting is one of the important topics in the data mining field, such as, predictions, weather forecasting, predictions of academic achievement. Another topic associated with forecasting through a series of data that depends on the time period is called time series. The problem in data mining time series is how to present the data. A common approach is to transform periodic series into other domains so that the reduced dimensions are followed by an index mechanism. However, research on time series has not been successful optimal yet, because it is still limited to mining data yet to represent time series, this pattern needs to be developed to change the pattern into a rule. The main problem that needs to be addressed in a periodic series is to present the results of visualization which includes more than thousands of observations are very difficult in order to present time series data in multidimensional to be mined. Working with high-dimensional data will be very expensive in terms of process and storage costs, because it requires high-level data representation or abstraction. The method used is the cluster window. The results obtained are the discovery of patterns based on the frequency of symbol behavior
NUIG at the FinSBD Task: Sentence Boundary Detection for Noisy Financial PDFs in English and French

Portable Document Format (PDF) has become the industry-standard document as it is independent of the software, hardware or operating system. Pub-licly listed companies annually publish a variety of reports and too take advantage of PDF. This leads to the rise in PDF containing valuable ﬁnancial information and the demand for approaches able to accurately extract this data. Analyzing and mining information requires a challenging extraction phase, particularly with respect to document structure. In this paper, we describe a sentence boundary detection approach capable of extracting complete sentences from unstructured lists of tokens. Our approach is based on the application of a language model and sequence classiﬁer for both the English and the French language. The results show a good performance, achieving F1 scores of 0.855 and 0.91, and placed our team in 3 rd and 5 th for the French and English language, respectively.
COLLABORATIVE PROJECT-BASED LEARNING: AN EXPERIENCE

This paper presents, from the point of view of learning, the results obtained in a subject that is taught following the philosophy of project-based learning. Each academic year, all the students of the teaching group of the Multimedia Technologies at the University of Almería develop, in a controlled manner in time and in procedure, a novel project (all students work in parallel on the same project), following a strict protocol based on the resolution of small issues, analysis, and adoption of the best solution achieved, with the idea of continuing to implement the project based on the best found solution until that moment. With this methodology (an iterative process of issues definition, proposals implementation, in-classroom presentation of the proposals, and adoption of the best proposal), the students develop their skills to solve problems, work in groups, increase their critical sense, speak in public, and get an idea of the way of working with which they will find in many companies, especially in those related to technology. In addition, thanks to the continuous evaluation process, the students know their performance in the subject as they are studying it, which significantly increases the percentage of success. The obtained results compare objectively different academic parameters, such as the number of students enrolled and the ratio of students who pass, before and after this new form of learning, showing a positive tendency.