Miracle-dz/UniFAR-FTUs huggingface dataset:
SPt: A Text Mining Process to Extract Relevant Areas from SW Documents to Exploratory Tests

Software products must show high-quality levels to succeed in a competitive market. Usually, products reliability is assured by testing activities. However, SW testing is sometimes neglected by Companies due to its high costs - particularly when manually executed. In this light, this work investigates intelligent methods for SW testing automation, focusing on the software products review process. We propose a new process for test plan creation based on the inspection of SW documents (in particular, Release Notes) using text mining techniques. The implemented prototype, the SWAT Plan tool (SPt), automatically extracts from Release Notes relevant areas of the SW to be examined by exploratory tests teams. SPt was tested using real-world data from Motorola Mobility, our partner Company. The experiments compared the current manual process with the automated process using SPt, accessing time spent and relevant areas identified in both methods. The obtained results were very encouraging.
Marshallian Forces and Governance Externalities: Location Effects on Contractual Safeguards in Research and Development Alliances

We examine the impact of geographic location of alliance activities on the design of safeguards in contracts governing research and development (R&D) partnerships. Joining research on agglomeration and alliance governance, we argue that the Marshallian agglomerative forces at work in a given location produce governance-related externalities that extend beyond productivity-related externalities considered in previous research. We investigate how location characteristics linked to Marshallian forces, such as local knowledge spillovers, R&D rivalry, dense industry employment, and the strength of professional organizations, have an impact on the specification of formal governance mechanisms. In particular, these Marshallian forces have a bearing on formal governance mechanisms that safeguard the execution of the R&D partnership, such as joint administrative interfaces and termination provisions. We analyze R&D partnerships between biotechnology and pharmaceutical firms and find that misappropriation hazards arising from greater knowledge spillovers and R&D competition in the region where R&D activities are located promote the use of these formal governance mechanisms in R&D partnerships. We also find that factors supporting thick interpersonal networks, such as the intensity of sectoral employment and the strength of professional bodies, reduce the use of formal governance mechanisms in R&D partnerships.
Single-trial prediction of unilateral isometric force levels from EEG signals

The extent of physiological circuits involved in the increasing isometric force levels during unilateral contraction has been shown to be different. Here, we explore the possibility of force level prediction based on electroencephalogram (EEG) activity recorded during a single trial ofunilateral 5% or 40% of maximal isometric voluntary contraction (MVC) into right hand grip movement and grip imagination. There are eleven healthy subjects involved in this study. The subjects were required to randomly perform 20 trials of each force level during right hand grip movement or grip imagination. We proposed to use common spatial patterns and coherence between EEG signals as the features to support vector machine for force level prediction. The results shown that the force levels can be predicted through single-trial EEG during unilateral isometric hand grip (mean accuracy=69.88± 7.24%) as well as during grip imagination (mean accuracy=64.8 9.05%). This may provide additional control commands for robot being controlled by users through brain-computer interface.
Bending and Wrinkled Model Research of a Soft Robot Inspired by Octopus

A concise numerical analysis method was proposed in this article, which is for bending and wrinkled model of a soft robot inspired by octopus. According to the curved geometry and simplified model of the soft robot, the bending model of the soft robot is established and the mapping relationship between the elongation of the spring model and the bending parameters (bending direction $\phi $, bending angle $\theta $, bending radiusR0) of the robot is obtained. Based on the wrinkled model of the inflatable beam structure, the main cause of failure of the robot under external force was analyzed, the wrinkling, failure and critical state of the robot were defined, and the wrinkled model of the soft robot was established. The experimental platform was built to carry out the wrinkle experiment under external load for the base section.
Advanced Automation for SMEs in the I4.0 Revolution: Engineering Education and Employees Training in the Smart Mini Factory Laboratory

Industry 4.0 is currently changing the shop-floor of many large manufacturing companies. Small and medium-sized enterprises are also increasingly concerned with the introduction of Industry 4.0 technologies and methods. This requires a great change of employees, creating a strong demand for training and further education for existing workers. The qualification of employees of small and medium-sized enterprises as well as engineering students can take place, for example, in learning factories where a revised educational paradigm is implemented. In this paper, the experiences and results we have learnt with the implementation of specific trainings and educational activities for advanced automation in the Smart Mini Factory Laboratory of the Free University of Bolzano are explained and discussed.
A Robot Model That Obeys a Norm of a Human Group by Participating in the Group and Interacting with Its Members

SUMMARY Herein, we proposed a robot model that will obey a norm of a certain group by interacting with the group members. Using this model, a robot system learns the norm of the group as a group member itself. The people with individual di ﬀ erences form a group and a characteristic norm that reﬂects the group members’ personalities. When robots join a group that includes humans, the robots need to obey a characteristic norm: a group norm. We investigated whether the robot system generates a decision-making criterion to obey group norms by learning from interactions through reinforcement learning. In this experiment, human group members and the robot system answer same easy quizzes that could have several vague answers. When the group members answered di ﬀ er-ently from one another at ﬁrst, we investigated whether the group members answered the quizzes while considering the group norm. To avoid bias toward the system’s answers, one of the participants in a group only obeys the system, whereas the other participants are unaware of the system. Our experiments revealed that the group comprising the participants and the robot system forms group norms. The proposed model enables a social robot to make decisions socially in order to adjust their behaviors to common sense not only in a large human society but also in partial human groups, e.g., local communities. Therefore, we presumed that these robots can join human groups by interacting with its members. To adapt to these groups, these robots adjust their own behaviors. However, further studies are required to reveal whether the robots’ answers a ﬀ ect people and whether the participants can form a group norm based on a robot’s answer even in a situation wherein the participants recognize that they are interacting in a group that include a real robot. Moreover, some participants in a group do not know that the other participant only obeys the system’s decisions and pretends to answer questions to prevent biased answers.
Strategies for Short Text Representation in the Word Vector Space

Short texts are present in many computer systems. Examples include social media messages, advertisement, Q&A websites, and an increasing number of other applications. They are characterized by little context words and a large vocabulary. As a consequence, traditional short text representations, such as TF and TF-IDF, have high dimensionality and are very sparse. The research field of word vectors has produced interesting word representations that are discriminative regarding semantics, which can be algebraically composed to create vector representations for paragraphs and documents. Literature reports limitations of this approach, producing the alternative Paragraph Vector method. Firstly, we investigate whether these limitations involving word vector operations are true for short text. Then, we propose a novel representation method based on the PSO meta-heuristic. Results in a document classification task are competitive with TF-IDF and show significant improvement over Paragraph Vector, with the advantage of dense and compact document vector representation.
Memetic networks: problem-solving with social network models

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Content Analysis Approach: A Review on the Extent of Science and Engineering Curriculum Meet Competency Requirements for Testing, Inspection and Certification Industry

Testing, Inspection, and Certification (TIC) are adopted to examine if a product conforms to regulatory standards respected to safety, quality and functionality that are globally accepted. Nowadays, testing, inspection and certification applied in products aims to verify the quality of product so that consumers can make proper decision and consideration in the selection of products. Due to the importance of testing, inspection and certification, market trend, competency of practitioners engaged in TIC and the methodology to study the readiness of their engagement are discussed. This paper reviewed the market trend of the testing, inspection and certification industry, discussing competency standards for practitioners engaging in TIC industry. Three main areas are studied: testing, inspection and certification industry market trend, competency requirements and content analysis methodology.
Screw Dynamic Modeling and Dynamic Sliding Mode Control for a Hybrid Mechanism

For a hybrid mechanism for automobile electro-coating conveying which is self-developed, a dynamic model is established based on screw theory and the principle of virtual work to simplify the process of modeling. Then, to solve the chattering problem and the slow rate of convergence problem of the existing sliding mode control methods for the hybrid mechanism, a super-twisting algorithm based second-order nonsingular fast terminal sliding mode control method is proposed. The stability of the proposed control method is proved by Lyapunov stability theorem. Finally, compared with the reaching law based sliding mode control method and the super-twisting algorithm based second-order sliding mode control method, it is indicated from the simulation and experiment results that the proposed control method not only has high steady-state accuracy, better dynamic performance and strong robustness, but also reduces the chattering and improves the rate of convergence of the system. And it lays foundation for realizing the high performance control of the hybrid mechanism for automobile electro-coating conveying.
On the design and implementation of a class of multiplierless two-channel 1D and 2D nonseparable PR FIR filterbanks

This paper proposes a new design and implementation method for a class of multiplierless 2-channel 1D and 2D nonseparable perfect reconstruction (PR) filterbanks (FB). It is based on the structure proposed by S.M. Phoong et al. (see IEEE Trans. Sig. Proc., vol.43, p.649-64, 1995) and the use of multiplier blocks (MB). The latter technique allows one to further reduce the number of adders in implementing these multiplier-less FB by almost 50%, compared to the conventional method using sum of powers of two coefficients (SOPOT) alone. Furthermore, by generalizing the 1D to 2D transformation of Phoong et al., new 2D PR FBs with quincunx, hourglass, and parallelogram spectral support are obtained. These nonseparable FBs can be cascaded to realize new multiplierless PR directional FB for image processing and motion analysis. Design examples are given to demonstrate the usefulness of the proposed method.
Brain Utilization of MNCs in Japan Compared with that of Japanese Companies Overseas

Multi-national Companies (MNCs) globalize their operation including research and development (R&D). They utilize local brains when conducting R&D. This paper discuss their brain utilization in Japan compared with that of Japanese companies overseas by analyzing the locations of inventors of intellectual property rights (IPRs) registered in the United States. The paper found out that MNCs started using Japanese brains to create IPRs very early and that Japanese companies started using overseas brains to create IPRs quite late. However, Japanese companies are very active in using overseas brains recently.
Solving a Spatial Puzzle Using Answer Set Programming Integrated with Markov Decision Process

Spatial puzzles are interesting domains to investigate problem solving, since the reasoning processes involved in reasoning about spatial knowledge is one of the essential items for an agent to interact in the human environment. With this in mind, the goal of this work is to investigate the knowledge representation and reasoning process related to the solution of a spatial puzzle, the Fisherman's Folly, composed of flexible string, rigid objects and holes. To achieve this goal, the present paper uses heuristics (obtained after solving a relaxed version of the puzzle) to accelerate the learning process, while applying a method that combines Answer Set programming (ASP) with Reinforcement learning (RL), the oASP(MDP) algorithm, to find a solution to the puzzle. ASP is the logic language chosen to build the set of states and actions of a Markov Decision Process (MDP) representing the domain, where RL is used to learn the optimal policy of the problem.
Fast Visual Odometry Based Sparse Geometric Constraint for RGB-D Camera

Pose estimation is a basic requirement for the autonomous behavior of robots. In this article we present a robust and fast visual odometry method to obtain camera poses by using RGB-D images. We first propose a motion estimation method based on sparse geometric constraint and derive the analytic Jacobian of the geometric cost function to improve the convergence performance, then we use our motion estimation method to replace the tracking thread in ORB-SLAM for improving its runtime performance. Experimental results show that our method is twice faster than ORB-SLAM while keeping the similar accuracy. key words: pose estimation, fast visual odometry, geometric cost function, iterative optimization, 3D reconstruction
Design and synthesis of improved reversible circuits using AIG- and MIG-based graph data structures

Reversible logic synthesis is one of the best suited ways which act as the intermediate step for synthesising Boolean functions on quantum technologies. For a given Boolean function, there are multiple possible intermediate representations (IRs), based on functional abstraction, e.g. truth table, decision diagrams or circuit abstraction, e.g. binary decision diagram (BDD), and-inverter graph (AIG) and majority inverter graph (MIG). These IRs play an important role in building circuits as the choice of an IR directly impacts on cost parameters of the design. In the authors' work, they are analysing the effects of different graph-based IRs (BDD, AIG and MIG) and their usability in making efficient circuit realisations. Although applications of BDDs as an IR to represent large functions has already been studied, here they are demonstrating a synthesis scheme by taking AIG and MIG as IRs and making a comprehensive comparative analysis over all these three graph-based IRs. In experimental evaluation, it is being observed that for small functions BDD gives more compact circuits than the other two IRs but when the input size increases, then MIG as IR makes substantial improvements in cost parameters as compared with BDD by reducing quantum cost by 39% on an average. Along with the experimental results, a detailed analysis over the different IRs is also included to find their easiness in designing circuits.
Hybridization of Genetic and Group Search Optimization Algorithm for Deadline-Constrained Task Scheduling Approach

Abstract Cloud computing is an emerging technology in distributed computing, which facilitates pay per model as per user demand and requirement. Cloud consists of a collection of virtual machines (VMs), which includes both computational and storage facility. In this paper, a task scheduling scheme on diverse computing systems using a hybridization of genetic and group search optimization (GGSO) algorithm is proposed. The basic idea of our approach is to exploit the advantages of both genetic algorithm (GA) and group search optimization algorithms (GSO) while avoiding their drawbacks. In GGSO, each dimension of a solution symbolizes a task, and a solution, as a whole, signifies all task priorities. The important issue is how to assign user tasks to maximize the income of infrastructure as a service (Iaas) provider while promising quality of service (QoS). The generated solution is competent to assure user-level (QoS) and improve Iaas providers’ credibility and economic benefit. The GGSO method also designs the producer, scrounger ranger, crossover operator, and suitable fitness function of the corresponding task. According to the evolved results, it has been found that our algorithm always outperforms the traditional algorithms.
Different revegetation types alter soil physical-chemical characteristics and fungal community in the Baishilazi Nature Reserve

The effects of different revegetation types on soil physical–chemical characteristics and fungal community diversity and composition of soils sampled from five different revegetation types (JM, Juglans mandshurica; QM, Quercus mongolica; conifer-broadleaf forest (CB); LG, Larix gmelinii; PK, Pinus koraiensis) in the Baishilazi Nature Reserve were determined. Soil fungal communities were assessed employing ITS rRNA Illunima Miseq high-throughput sequencing. Responses of the soil fungi community to soil environmental factors were assessed through canonical correspondence analysis (CCA) and Pearson correlation analysis. The coniferous forests (L. gmelinii, P. koraiensis) and CB had reduced soil total carbon (C), total nitrogen (N), and available nitrogen (AN) values compared with the broadleaf forest (J. mandshurica, Q. mongolica). The average fungus diversity according to the Shannon, ACE, Chao1, and Simpson index were increased in the J. mandshurica site. Basidiomycota, Ascomycota, Zygomycota, and Rozellomycota were the dominant fungal taxa in this region. The phylum Basidiomycota was dominant in the Q. mongolica, CB, L. gmelinii, and P. koraiensis sites, while Ascomycota was the dominant phylum in the J. mandshurica site. The clear differentiation of fungal communities and the clustering in the heatmap and in non-metric multidimensional scaling plot showed that broadleaf forests, CB, and coniferous forests harbored different fungal communities. The results of the CCA showed that soil environmental factors, such as soil pH, total C, total N, AN, and available phosphorus (P) greatly influenced the fungal community structure. Based on our results, the different responses of the soil fungal communities to the different revegetation types largely dependent on different forest types and soil physicochemical characteristic in Baishilazi Nature Reserve.
Ptosis of the hip: a new radiographic finding in patients undergoing femoroacetabular osteoplasty

ABSTRACT Ptosis is a newly described phenomenon appearing on AP radiographs of patients undergoing femoroacetabular osteoplasty (FAO), and refers to a reverse break down in Shenton’s Line. Thorough characterization of this phenomenon is needed to better understand the hip morphology and pathologic ramifications. Our goal was to define the radiographic hip parameters accompanying a break down in Shenton’s Line and to determine how these values compare with standard values in normal hips. Using two independent readers, we retrospectively reviewed the medical records and preoperative supine radiographs of 630 patients (1260 hips) who underwent FAO by a single surgeon between 2003 and 2016. Prevalence of hip pathology and 28 radiographic parameters in ptosis hips was measured, as well as a comparison between unilateral ptosis hips and contralateral normal hips. Of the 53 patients (106 hips) who fulfilled the criteria for the study, 94 hips had a Shenton’s Line break down of at least 5 mm. Sixty-nine percent of ptosis hips had femoroacetabular impingement (FAI), 70.2% had coxa profunda, and 52.1% had partial joint space narrowing. Ptosis hips had 1.05 mm less lateral subluxation (P = 0.012), 2.28° larger Center-edge angle (P = 0.046), 2.59° smaller Sharp angle (P = 0.011) and 2.49% smaller extrusion index (P = 0.016) compared with contralateral normal hips. FAI is prevalent in patients with a positive ptosis sign. The high prevalence of partial joint space narrowing could suggest eventual osteoarthritis. We believe our results demonstrate the importance of further investigation of a positive ptosis sign on AP pelvic radiographs.
Cell penetrating peptides in preclinical and clinical cancer diagnosis and therapy

Delivery of imaging reagents and drugs to tumors is essential for cancer diagnosis and therapy. In addition to therapeutic and diagnostic functionalities, peptides have potential benefits such as biocompatibility, ease to synthesize, smaller size, by-passing off-target side effects, and achieving the beneficial effects with lower-administered dosages. A particular type of peptide known as cell penetrating peptides (CPP) have been predominantly studied during last twenty years as they are not only capable to translocate themselves across membranes but also allow carrier drugs to translocate across plasma membrane, by different mechanisms depending on the CPP. This is of great potential importance in drug delivery systems, as the ability to pass across membranes is crucial to many drug delivery systems. In spite of significant progress in design and application of CPP, more investigations are required to further improve their delivery to tumors, with reduced side-effect and enhanced therapeutic efficacy. In this review, we emphasis on current advancements in preclinical and clinical trials based on using CPP for more efficient delivery of anti-cancer drugs and imaging reagents to cancer tissues and individual cells associated with them. We discuss the evolution of the CPPs-based strategies for targeted delivery, their current status and strengths, along with summarizing the role of CPPs in targeted drug delivery. We also discuss some recently reported diagnostic applications of engineered protease-responsive substrates and activable imaging complexes. We highlight the recent clinical trial data by providing a road map for better design of the CPPs for future preclinical and clinical applications.