Miracle-dz/UniFAR-FTUs huggingface dataset:
A Survey on Robot Teaching: Categorization and Brief Review

In this paper, a review of current work within in the field of robot teaching has been presented. Researchers in the robot teaching domain suffer from the lack of an establishedstructure in which to organize appropriate teaching systems. We have contributed such a structure, througha categorization of robot teaching technologies. We first segmentthe robot teaching technologies according to required programmingskill, then divided into two types according to theteaching layer. As a result we identified several openissues that need to be addressed to design a robot teachingsystem. The robot teaching technology has been growing significantly,from text-based programming systems to teaching bydemonstration and more intelligent systems that provideconsiderable support to the human teacher. Clearly, the trendin robot teaching technologiesis the growing intelligence toreduce the burden of human teacher, both for manual andautomatic teaching method.
A DENSITY-BASED MICRO AGGREGATION TECHNIQUE FOR PRIVACY-PRESERVING DATA MINING

Microaggregation is an effective means of protecting the microdata in the statistical databases. Microaggragation protects the microdata by partitioning them into groups of at least k records in each group and substituting the records in each group with the centroid of the group. An optimal microaggregation can be achieved by minimizing the information loss incurred from the aggregation. This paper presents a density-based microagregation method for protecting the numeric data employing the densitybased notion of clustering. In this work we provide a microaggragation method which reduces the risk of microdata disclosure and incurs the minimum information loss with incresed data utility. In addition to it the paper also shows an experimental compariosn with the existing heuristics for microaggregation.
Personality through faces for humanoid robots

Empirical studies have shown the great extent to which a face characterizes personality. For this reason, among others, the face of a humanoid robot is paramount. In fact, a social contract exists between the robot and each human it interacts with. We present the design issues we have encountered in designing a face for the humanoid robot Cog. The dimensions, organic form and mechanical constraints must be worked out to attain our goals. Our implementation of the design process is novel with respect to our use of solid modeling and stereo lithography processes and has proven to be essential to our quest for an iconic human-like face.
Age-Segregated and Gated Retirement Communities in the Third Age: The Differential Contribution of Place — Community to Self-Actualization

The author focuses on the role of place-based community both in in-situ aging and in age-segregated (often gated and walled) retirement villages within cities as a potential contributor towards self-actualization. Elderly individuals in a case study in Calgary, Alberta, were measured on the short index of self actualization and a series of multivariate ‘structures’ of place — community associated with behavioral, cognitive, and affective features of community derived from a principal components analysis of community indicator variables. Self-actualization tendencies were not found to differ by residential context. Multiple regression models showed a similar overall contribution of all community structures to self-actualization in the different residential contexts, but different sets of community structures were identified as unique and significant predictors of self-actualization in the two residential settings. This differential impact may signal that a variety of forms and structures of person–environment congruence amongst the elderly yield similar psychological outcomes.
A vascular network matching in dynamic thermography for breast cancer detection

Because of limitation of human visual system, computer-aided diagnosis has been playing an important role in early breast cancer detection. Clinical interpretation of dynamic breast thermograms first depends on the similarity analysis between vascular pattern in thermal images before and after applying a cold challenge. To analyse the thermal images for cancer detection purposes in dynamic breast thermography, a new approach is proposed. First, the breast region was delineated from the background using a connected component method. Then, the blood vessels presented on the segmented breast region were extracted using image morphology. The branching points of the skeletonised vascular network are referred to as thermal minutia points (TMPs). Similar to fingerprints, these feature points are used as a geometric representation of the shape of vein patterns during the classification stage. Local and global structures of TMPs which are extracted from a cold-challenged image are matched with those of baseline image. The experimental results showed that the algorithm reaches 86% of the sensitivity and 61% specificity from 50 thermograms (25 normal and 25 cancerous). The mean of matching scores in malignant and benign cases are 0.78 and 0.3, respectively. According to the results the proposed approach is promising for detection of breast abnormality. More importantly, the results indicated the possibility of this framework in breast cancer detection so as to open a proper way for further methodological and experimental research in this area.
Mass communication and journalism faculty’s perceptions of the effectiveness of email communication with college students: a nationwide study

Nearly 700 US journalism and mass communication faculty (all teaching personnel) reported their perceptions of student email use via a web‐based survey. This nationwide study focused on the content of email sent by faculty to students, email’s effectiveness, and email’s effect on student learning. Comparisons were made based on faculty gender, rank, age, and ethnicity. Findings suggest that despite statistical differences, when gender, rank, age, or ethnicity are considered, faculty are not in the habit of sending course materials like syllabi, project instructions, and lecture notes to students personally via email. Moreover, faculty tend to find favor with email communication and its effectiveness as a tool of teaching. The results of this survey coupled with previous research by the authors and other scholars suggest faculty ought to embrace the technology and develop positive ways to incorporate email, as well as other technology, into the educational process.
An Efficient Buffer Replacement Policy based on CLOCK Algorithm for NAND Flash Memory

ABSTRACT Recently, NAND flash memory has been popular for a next-generation storage device because of its rapid access speed, low-power consumption, shock-resistant. However, unlike a disk based storage device, it has distinct characteristics such as the asymmetric cost of read, write and erase operation, and no in-place update. thus, disk based buffer management policies may not yield a good performance on NAND flash memory based system. To solve this problem, flash-aware buffer management policies which consider the characteristics of NAND flash memory have been proposed.In this paper, we propose a novel flash-aware buffer replacement policy called CLOCK-NAND that is an enhancement of the well-known disk-based buffer replacement policy, CLOCK-Pro. Although CLOCK-NAND is basically based on the algorithm of CLOCK-Pro, it exploits a new hot page change policy to use page access information efficiently. Also, it delays write operation for dirty and hot page by using cold change delay policy and it yield a good performance on NAND flash memory according to these new policies. Keywords:Flash Memory, Buffer Management Policy, Clock-Pro, Hot Page, Dirty Page
How would photons describe natural phenomena based upon their physical experiences?

The question posed in the title represents an impossible approach to scientific investigation, but the approach is like a subjectivist. Obviously, photons cannot express their views; neither can we ask directly any scientific questions to the photons. The purpose is to draw the attention of the reader that even our strongly mathematically driven scientific enterprise is full of subjectivism when we start dissecting our thinking process. First, we frame questions in our mind to understand a natural phenomenon we have been observing. Let us not forget that framing the question determine the answer. The answers guide us to frame the foundational hypotheses to build a theory to “explain” the phenomenon under study. Our mind is a product of biological evolutionary requirements; which is further re-programmed by strong human social cultures. In other words, human constructed theories cannot spontaneously become rigorously objective, unless we consciously make them so. We need to develop a methodology of scientific thinking that will automatically force us to make repeated iterative corrections in generating questions as objectively as possible. Those questions will then guide us to re-construct the foundational hypotheses and re-frame the working theories. We are proposing that we add Interaction Process Mapping Epistemology (IPM-E) as a necessary extra thinking tool; which will complement the prevailing Measurable Data Modeling Epistemology (MDM-E). We believe that ongoing interaction processes in nature represent reality ontology. So the iterative application of IPM-E, along with MDM-E, will keep us along the route of ontological reality. We apply this prescription to reveal the universal property, Non-Interaction of Waves, which we have been neglecting for centuries. Using this property, we demonstrate that a large number of ad hoc hypotheses from Classical-, QM-, Relativity- and Astro-Physics can be easily modified to make physics more causal and understandable through common sense logics.
Corn leaf disease spot recognition comparative study of Bayesian classification and fuzzy pattern recognition

Crop diseases occurrence have a great impact on Agricultural Production. Using the technology based on machine recognition to identify crop diseases automatically has important significance on agricultural production. The principles of the Bayesian Classification and the Fuzzy Pattern Recognition are introduced in this paper. Classification on 5 kinds of corn leaf diseases spot respectively are implemented based these two methods. The results show that the average recognition rate of Fuzzy Pattern Recognition is higher than Bayesian Classification’s on corn leaf disease spot. Average recognition rate of the 5 kinds of corn leaf disease spot is more than 93%.
Noisy speech recognition using hidden Markov model state-based filtering

A system which exploits this property of hidden Markov models (HMM) in order to implement effective noise canceling filters within the speech recognition task is described. The filtering process reduces the sensitivity of the recognition system to input fluctuations caused by noise. The system developed uses continuous probability density function, single Gaussian mixture HMMs trained on filterback output vectors. Using autocorrelation statistics collected for speech and noise during training, noise canceling Wiener filters are designed for each hidden Markov model state. The resulting system outperforms by a significant margin results obtained using clean-speech HMMs on either noisy speech or noisy speech with the noise mean subtracted. <<ETX>>
Pedestrian Detection Optimization Algorithm Based on Low-Altitude UAV

Pedestrian detection is one of the critical benchmarks for object detection in computer vision. In recent years, more effective detectors and features, such as Histograms of Oriented Gradients (HOG) have been proposed. The process of HOG features calculation is slow, and the features cannot satisfy represent the human body. Therefore, we adopt the multi-channel features, and propose a new improved method for accelerated integral image, the execution time of which is less than the original method. In addition, we apply novel multi-scales detection to detect new scenario, which is based on the low-altitude UAV. Under such scenario our algorithm can handle the changing in pedestrian posture and occlusion cues. The experimental results indicate that our algorithm is rapid and efficient under dynamic camera, comparing with other methods in INRIA dataset.
CHARACTERIZATION AND QUANTIFICATION OF PROTEINS IN WHEY OBTAINED AS A BY-PRODUCT FROM WHITE CHEESE AND YELLOW CHEESE PRODUCTION

After the cheese production process, the whey, obtained as a by-product, is not valorised and remains in the waste water which is usually disposed of in natural watercourses. The aim of the study was to analyse the profile of whey proteins, as well as, to quantify the amount of those fractions. 12.5 % SDS-PAGE was used. The total amount of proteins in whey from cow white cheese was 0.73 % ± 0.15, while in cow kashkaval whey was 0.91 % ± 0.08. In whey from white cheese, the relative protein percentages were: lactoglobulin 67.29 % ± 4.99, lactalbumin 20.64 % ± 2.02 and other fractions related to bovine serum albumin with 12.07 % ± 3.05. In whey from yellow cheese, the proteins percent-ages were: lactoglobulin 52.62 % ± 1.21, lactalbumin 17.62 % ± 1.26 and other fractions related to bovine serum albu-min with 29.74 %, respectively. Predominantly, -lactoglobulin was present in the analysed samples. The valorisation of the waste whey obtained in the white cheese production, and development of new product also contributes in the en-vironment protection.
A two-phase approach for jointly determining the lot size and delivery policy in a vendor-buyer integrated system with rework

This paper presents a two-phase algebraic approach for jointly determining the lot size and delivery policy in a vendor-buyer integrated system with rework. Conventional method uses the diff erential calculus for solving production-shipment problem with the need for proving optimality fi rst on the system cost function. This study proposes a two-phase algebraic solution procedure to derive the optimal lot size as well as optimal number of deliveries without using derivatives. The proposed approach enables practitioners who maybe without suff icient knowledge of calculus to understand such a specifi c real world problem with ease
3D Object Classification Using a Two-Dimensional Hidden Markov Model

In this paper, we develop a novel method of 3D object classification based on a Two-Dimensional Hidden Markov Model (2D HMM). Hidden Markov Models are a widely used methodology for sequential data modeling, of growing importance in the last years. In the proposed approach, each object is decomposed by a spiderweb model and a shape function D2 is computed for each bin. These feature vectors are then arranged in a sequential fashion to compose a sequence vector, which is used to train HMMs. In 2D HMM, we assume that feature vectors are statistically dependent on an underlying state process which has transition probabilities conditioning the states of two neighboring bins. Thus the dependency of two dimensions is reflected simultaneously. To classify an object, the maximized posteriori probability is calculated by a given model and the observed sequence of an unknown object. Comparing with 1D HMM, the 2D HMM gets more information from the neighboring bins. Analysis and experimental results show that the proposed approach performs better than existing ones in database.
Metadata Update Strategy with High Reliability

The traditional metadata cluster write reliability was guaranteed by the large journals. If suddenly the system is powered down, the system crashes, failure of the storage medium bit error in the incident of writing the data and it could cause a loss of metadata and resulting in corruption of the reliability. This will have a huge negative impact on the user application. In this paper we propose a method of Asymmetric Based Update Strategy (ABUS), which can be a good safeguard to keep reliable of the metadata update and can solve the problem of inconsistent between the memory and the disk. It can effectively increasing the metadata cluster update reliability by the way of self-healing in the case of failure and ensure the efficiency of the metadata I/O.
STUDI KOMPARASI METODE KLASTERISASI DATA K-MEANS DAN K-HARMONIC MEANS

Salah satu metode partitional clustering yang sangat popular adalah K-Means Clustering (KM). Metode ini banyak digunakan karena implementasinya yang sederhana, dapat menangani data dalam jumlah besar dan proses yang relatif singkat. Meskipun demikian jika diperhatikan dari tahapan KM untuk mendapatkan klaster akhir masih terdapat kelemahan. Titik awal pusat klaster pada KM yang ditentukan secara random dan proses pembaharuan titik pusatnya sangat memungkinkan hasil klaster konvergen pada lokal optimal.K-Harmonic Means Clustering (KHM) merupakan algoritma yang diciptakan untuk menyempurnakan KM. Dalam KHM titik pusat diperbaharui dengan memanfaatkan rata-rata harmonik dari seluruh titik data ke seluruh pusat klaster yang ada. Rata-rata harmonik dalam metode KHM digunakan untuk mengurangi permasalahan yang ada pada KM. Pada penelitian ini dilakukan studi komparasi terhadap dua metode klasterisasi yaitu KM dan KHM. Penelitian ini ditujukan untuk melihat bagaimana performa metode KHM dalam menyempurnakan metode KM. Studi komparasi ini menggunakan lima buah data set.
User Classification Prediction Research Based on Clustering SVM

Users interest discovery can be belonged to category of classification knowledge discovery, which can divide user information need into two groups as interested group and uninterested group by classification forecast analysis of user information need which reflects historical visiting information behavior of user. After analysis the use of a several methods of user classification prediction, this paper presents a modified model combining clustering algorithm was presented for improving the forecasting accuracy of SVM. The efficiency of the proposed method was tested by the user access informations log data. The results have shown that the higher accuracy is expressed in this proposed model, and it is applicable to practice.
Simulation methodology using photographic terrain and rendered objects

We describe a simulation which has been developed over the last couple years for weapons trainers, in which a gunner can pan across a terrain scenario containing moving military vehicles. The terrain imagery is obtained by stitching together a panorama from photographs. Associated with this imagery are a wireframe model of the ground and a range image incorporating both the ground and other objects in the scene. This data allows military vehicles to be rendered at the correct scale and aspect for insertion in the scene, with occlusion by photographic scene objects as appropriate. Controls for contrast, brightness, and focus adjustment which are part of the weapons systems are included in the simulation, as well as rudimentary environmental effects. Achieving real-time performance with smooth user interaction on standard PC hardware posed some challenges. Our methodology assigns some of the processing steps and partial result storage to the graphics card, and others to the system CPU and memory. The methodology also makes frequent use of precomputed and previous fame partial results, so that most frames do not require all the processing step to be applied to the whole visible area. We present an overview of the frame construction methodology, and discuss in more detail the scheme for background precomputation of focus processing results.
The Meaning Makes It Fun

Abstract Museum games can be a powerful meaning-making experience for players, but only if we understand that what makes games fun is also what makes them meaningful. Renowned game designer Sid Meier (Civilization, Railroad Tycoon, Pirates) famously defined a good game as “a series of interesting choices. ” What makes choices interesting? The same things that make them meaningful: consequences, context, and a sawy appeal to human psychology. When designed well, these choices can make even mundane content meaningful. So imagine the possibilities when we use the stuff of museums—art, science, history, and technology—as our content and context. Whether creating a game or a simpler activity, we can draw on principles of game design to weave a series of interesting choices that honors our real-world content while engaging visitors in thoughtful ways. This article considers that most common type of “game”—the quiz—before analyzing the board game Monopoly for attributes of interesting choices, and finally discusses how those attributes are built into several games for cultural institutions.
Information and Communication Technologies in Small and Medium Scale Tourism Enterprises in Durban, South Africa

The objectives of this paper are to identify factors influencing the adoption of innovations, specifically e-commerce readiness and to identify specific barriers or obstacles to the use of ICT by small and medium tourism enterprises in Durban, KwaZulu-Natal Province, South Africa. Enterprises studied included guesthouses, lodges and self-catering, bed and breakfast, and tour operators. A survey research design was adopted and data collected through interviews, observation and administration of a structured questionnaire. The results reveal that high cost, limited funds and not knowing what to adopt are perceived as barriers to the diffusion of ICTs among the tourism service providers in the study.
Early experiences building a software quality prediction model

Early experiences building a software quality prediction model are discussed. The overall research objective is to establish a capability to project a software system's quality from an analysis of its design. The technical approach is to build multivariate models for estimating reliability and maintainability. Data from 21 Ada subsystems were analyzed to test hypotheses about various design structures leading to failure-prone or unmaintainable systems. Current design variables highlight the interconnectivity and visibility of compilation units. Other model variables provide for the effects of reusability and software changes. Reported results are preliminary because additional project data is being obtained and new hypotheses are being developed and tested. Current multivariate regression models are encouraging, explaining 60 to 80 percent of the variation in error density of the subsystems.