Reviewerly/exHarmony huggingface dataset:
Fireteam: a small-team development practice in industry

Software development is a collective undertaking, and the team’s efficiency is critical in development. In order to reduce project management overheads and improve productivity, a global information and communication technology enterprise institutionalizes an organization wide small-team practice, called fireteams, to tackle the problems arising from human and social aspects, such as amicability, talent, skill, and communications. This paper reports a mixed-method research, which combines archive analysis, interviews and survey, to empirically investigate the characteristics and impacts of fireteam in this industrial setting. We identify three categories of fireteam in terms of its demonstrated characteristics: ordinary agile team with extensions, single-function team, and entire life-cycle team; elaborate four key activities of fireteam, i.e. team formation, maintenance, communication, and meeting. Less communication and management overheads, higher agility & concurrency, and improved personal ability are the three important contributors that increase the productivity of fireteams. Whereas management & leadership effort, divergent understanding of fireteam, and self-organized team are discovered as the three major problems associated with fireteams. Although the benefits of fireteam can be observed from its adoption, this practice does not achieve the enterprise’s anticipations very well. Some considerations and recommendations are also discussed to improve this small-team practice.
Obstacle Avoidance in Path Following using Local Spline Relaxation

This paper presents a motion planning approach, Local Spline Relaxation with Local Hyperplanes (LSR-LH), for autonomous vehicles to search for time-optimal collision-free motion trajectories through environments with stationary and dynamic convex obstacles. These trajectories are piecewisely parameterized as a fourth order polynomial based on Runge-Kutta's fourth order integration scheme. Collision-free trajectories are guaranteed by defining separating hyperplanes between obstacles and the continuous time trajectory of the vehicle. An optimal control problem is solved with a receding horizon to include the latest information of the environment and to take into account model mismatches. Extensive numerical simulations are performed to show the potential of the method.
Research on MR virtual scene location method based on image recognition

In order to solve the problem of accurate positioning of mixed reality virtual scene in physical space, this paper, firstly, analyzes the positioning principle of mixed reality virtual scene. Secondly, based on the comparison among the three developer kits: ARToolKit, ARTag, and Vuforia and two image optimization algorithms: AHE and ACE, it makes sure to use Vuforia development tool to complete the signature-based tracking and registration task, and use ACE algorithm to optimize the signature-based image. It improves the efficiency, stability and accuracy of image recognition registration. Then the multi-target recognition and registration technology is used to realize the multi-location of virtual scene. Finally, Hololens glasses are used as the hardware carrier to verify the above method. The experimental results show that the above method not only realizes the precise location of MR virtual scene based on image recognition, but also ensures the absolute position of the virtual model in the real space, bringing users a more real virtual experience. Keywords-mixed reality, multi-person collaboration, virtual positioning, gesture interaction.
Attributed Transition-Based Domain Control Knowledge for Domain-Independent Planning

Domain-independent planning decouples a planning task specification from planning engines. As the specification is usually describing only the physics of the environment, actions and a goal, the planning engines being generic solvers designed to solve any planning task tend to struggle with tasks that can be easily solved by domain-specific algorithms. Additional control knowledge can, to large extent, bridge such a performance gap. Instead of providing a specific planner supporting a given form of control knowledge, control knowledge can be directly encoded within the planning task specification and thus can be exploited by generic planners. In this paper, we propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Attributed Transition-Based Domain Control Knowledge (ATB-DCK)</i> that is represented by a finite state automaton with attributed states, referring to specific states of objects, connected by transitions imposing constraints on action applicability. ATB-DCK, roughly speaking, represents the “grammar” of solution plans that guides the search. We show that ATB-DCK can be compiled into a classical planning task and thus it complements domain-independent planning techniques. Using several domains from the International Planning Competitions as benchmarks, we demonstrate that this approach often considerably improves efficiency of existing state-of-the-art planning engines.
Impact of hydrothermal alteration processes on element mobility and potential environmental implications at the Sousaki solfataric field (Corinthia - Greece)

Samples of efflorescences and encrustations of hydrothermal origin were collected at Sousaki (Greece) and analysed for their mineralogical (XRD) and chemical composition. Solutions obtained both from mineralization with HNO3 and from leaching with deionised water were analysed for major (ICP-OES), minor and trace metals (ICP-MS) and sulfate contents (IC). Results evidence the dependence of the chemical and mineralogical composition on micro-environmental conditions i.e. humidity, oxygen-rich or -poor environment, exposed or sheltered from meteoric agents. In fact, the presence of highly soluble sulfate minerals with elevated contents of many metals (e.g. Mg, Al, Fe, Mn, Cr, Ni, etc.) further underscores the important influence of hydrothermal activity on elements' mobility, whilst the sometimes very high concentrations in toxic elements like Al, Cr, Ni suggest also possible environmental impacts.
Comparative Analysis on Different Cepstral Features for Speaker Identification Recognition

Speaker recognition is an Artificial Intelligent (AI) technology that lets the machine to process, interpret and respond to human language. In this work, the recorded speech developed from a collection of audio speeches is used as a database. Mel-frequency cepstral coefficients (MFCC) and gammatone frequency cepstral coefficients (GFCC) are two different cepstral features used in this work. These extracted features are then used to train, validate and test the classifier. Support Vector Machine (SVM) is the classifier used in developing the speaker identification system. This classifier is trained to classify the input speech into one of the ethnicity classes: Malay, Chinese, Indian or Bumiputera. The results are based on the two different usages of cepstral features from the same speech utterances by speakers. Finally, the comparative analysis of the speaker identification system is made concerning features and classifier. The results revealed that a combination of GFCC and pitch as the feature vectors (Model 4) produced the highest accuracy rate of 86.1%.
Effects of Virtual Reality Mudslide Games with Different Usability Designs on Fifth-Grade Children’s Learning Motivation and Presence Experience

Mudslide disaster prevention education is important for children who face related life-threatening situations in their daily lives. The purpose of this study was to compare the effects of two virtual reality (VR) mudslide games with different usability designs on children’s presence experience and learning motivation. The games provided users with three options (riverside, bridge and hillside) for escape from a mudslide disaster. Sixty-six fifth-grade students in Taiwan participated; 18 students played Game 1 and 48 students played Game 2 individually for 10–15 min. A 24-item questionnaire assessing the students’ learning motivation and presence experience was administered after game play. Questionnaire responses indicated that the students had positive attitudes toward learning via the two games. Learning motivation, presence and total questionnaire scores were higher among students who played Game 2 than among those who played Game 1, but these differences were not significant. All three scores were higher among boys than among girls who played Game 2, as indicated by nonparametric analysis. The results indicate that the low-polygon VR mudslide game designed for usability facilitated children’s, and especially boys’, learning about mudslide disaster prevention.
ProDB: A memory-secure database using hardware enclave and practical oblivious RAM

One key challenge for data owners to host their databases in the cloud is data privacy. In this paper, we first demonstrate that even with the most recent hardware-based security technology such as Intel SGX, a hypervisor can still sniff key database operations running in its guest virtual machine (VM) such as the frequency and type of SQL queries, by monitoring the access pattern of this VM’s main and secondary memory. To ensure security against such access pattern monitoring attacks, we then propose ProDB, a minimal adaptation of a conventional DBMS with both hardware enclave and Oblivious RAM protocol. To enhance its performance for practical use, we also design a SQL-aware Path ORAM protocol called SaP ORAM, which optimizes the classic Path ORAM protocol under practical database workload. Through security analysis and extensive experimental results, we prove and show ProDB achieves high security and throughput on commodity cloud hosting servers.
Knowledge Extraction Method for Power Grid Fault Text Based on Ontology

Abstract The power industry usually records equipment failures, defects and other information in the form of text, which contains lots of regular patterns. Knowledge extraction in fault text is of great significance to improve efficiency and reduce the labor cost in the power industry. However, the research for knowledge extraction of text information in this field is rare, it is even more difficult to use machine learning algorithms to mine the deep patterns. To solve this problem, a method of knowledge extraction is proposed in this field. We use power equipment fault texts and relevant guidance as raw materials. Firstly, the knowledge base of this field is designed and constructed based on the ontology concepts, including ontology concept base, description base and regular expression base. Then, the knowledge extraction algorithm is designed according to the knowledge base. After that we conduct the knowledge merge operation to make the extraction results more accurate. Experiments on the real fault texts shows the feasibility and the high accuracy of our method when compared with artificial extraction.
The Role of Implicit Conversions in Erroneous Function Argument Swapping in C++

Argument selection defects, in which the programmer has chosen the wrong argument to a function call is a widely investigated problem. The compiler can detect such misuse of arguments based on the argument and parameter type in case of statically typed programming languages. When adjacent parameters have the same type, or they can be converted between one another, the potential error will not be diagnosed. Related research is usually confined to exact type equivalence, often ignoring potential implicit or explicit conversions. However, in current mainstream languages, like C++, built-in conversions between numerics and user-defined conversions may significantly increase the number of mistakes to go unnoticed. We investigated the situation for C and C++ languages where functions are defined with multiple adjacent parameters that allow arguments to pass in the wrong order. When implicit conversions are taken into account, the number of mistake-prone function declarations significantly increases compared to strict type equivalence. We analysed the outcome and categorised the offending parameter types. The empirical results should further encourage the language and library development community to emphasise the importance of strong typing and the restriction of implicit conversion.
Plagiarism Detection of Images

Plagiarism is when someone takes another author's works, thoughts, ideas, etc. without proper referencing and claim it as his/her own works. Plagiarism detection is the process to find the plagiarism within a work or documents. With the advance of modern technology, it makes it easier for people to search for information and plagiarize the work of others. Although the effort and ideas for an image-based plagiarism detection has been increasing over the years, flaws are still presence in the current systems. This paper proposes a new system that can cover those flaws. It consists three stages: the pre-processing, feature extraction and comparison stage. The results showed in an ascending order of similarity index and true and false. However, the accuracy is 100% in case of unedited images and variated in other operations such as flipped, rotated, greyscales and cropped.
A Dynamic Continuous Hand Gesture Detection and Recognition Method with FMCW Radar

In this paper, a continuous dynamic hand gesture detection and recognition method is proposed using a frequency modulated continuous wave (FMCW) radar. Specifically, we collect the raw radar data to estimate the radar intermediate frequency (IF) signal, and construct the range-time map (RTM) and Doppler-time map (DTM) with 2-Dimensional Fast Fourier Transform (2D-FFT). Then, we propose a hand gesture detection method, which obtains the amplitude of the normalized hand gesture and uses a threshold to effectively segment the continuous hand gesture. Finally, the hand gesture is recognized by the proposed Fusion Dynamic Time Warping (FDTW) algorithm based on the central time-frequency trajectory. Experiments with radar data show that the accuracy of the proposed hand gesture detection method can reach 96.17%, and compared with the traditional recognition algorithm, the proposed recognition algorithm can significantly improve the recognition accuracy rate (hand gesture average recognition accuracy rate can reach 94.50 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> ) with the time complexity reduced by more than 50%.
Recognition of occupational therapy exercises and detection of compensation mistakes for Cerebral Palsy

Depth camera-based virtual rehabilitation systems are gaining attention in occupational therapy for cerebral palsy patients. When developing such a system, domain-specific exercise recognition is vital. To design such a gesture recognition method, some obstacles need to be overcome: detection of gestures not related to the defined exercise set and recognition of incorrect exercises performed by the patients to compensate for their lack of ability. We propose a framework based on hidden Markov models for the recognition of upper extremity functional exercises. We determine critical compensation mistakes together with restrictions for classifying these mistakes with the help of occupational therapists. We first eliminate undefined gestures by evaluating two models that produce adaptive threshold values. Then we utilize specific negative models based on feature thresholding and train them for each exercise to detect compensation mistakes. We perform various tests using our method in a laboratory environment under the supervision of occupational therapists.
Cyber-Physical Security Evaluation in Manufacturing Systems with a Bayesian Game Model

Cyber-physical security has been taken more seriously recently in manufacturing systems because of highly increasing integration of cyber and network systems with physical production known as cyber-physical manufacturing systems (CPMS). This paper presents a novel proactive approach to forecast the interrelation of attackers and cyber-physical manufacturing systems to suggest a proper defense strategy to the system managerial. In this research, the zero-sum game payoff function developed in our previous studies is transformed into an epistemic type Bayesian game that addresses uncertainties on types of players. The risk preference behavior also is considered as a characteristic of different types of players and to form the Bayesian utility function. Finally, a numerical example is analyzed with a structured method by developing the Bayesian game, computing Bayes-Nash equilibria, and finding quantal response equilibrium. This approach enables managers to decide a proper defense strategy in advance of experiencing cyber-physical dilemma when there are not enough previous data.
$$\mathtt{LODsyndesis}_{IE}$$: Entity Extraction from Text and Enrichment Using Hundreds of Linked Datasets

We shall demonstrate $$\mathtt{LODsyndesis}_{IE}$$ , which is a research prototype that offers Entity Extraction from text and Entity Enrichment for the extracted entities, using several Linked Datasets. $$\mathtt{LODsyndesis}_{IE}$$ exploits widely used Named Entity Extraction and Disambiguation tools (i.e., DBpedia Spotlight, WAT and Stanford CoreNLP) for identifying the entities of a given text, and enriches each identified entity with hyperlinks to LODsyndesis, which offers various services for millions of entities by leveraging hundreds of Linked Datasets. $$\mathtt{LODsyndesis}_{IE}$$ brings several benefits to the entity extraction task: the user can a) annotate the entities of a given text by selecting different entity recognition tools, b) retrieve all the URIs and facts of each recognized entity from multiple datasets, and c) discover the K most relevant datasets (e.g., datasets containing the most facts) for each entity. The demo is available at https://demos.isl.ics.forth.gr/LODsyndesisIE/ .
Tighter generalized monogamy and polygamy relations for multiqubit systems

We present a different kind of monogamy and polygamy relations based on concurrence and concurrence of assistance for multiqubit systems. By relabeling the subsystems associated with different weights, a smaller upper bound of the $$\alpha $$th ($$0\le \alpha \le 2$$) power of concurrence for multiqubit states is obtained. We also present tighter monogamy relations satisfied by the $$\alpha $$th ($$0\le \alpha \le 2$$) power of concurrence for N-qubit pure states under the partition AB and $$C_1 \cdots C_{N-2}$$, as well as under the partition $$ABC_1$$ and $$C_2\cdots C_{N-2}$$. These inequalities give rise to the restrictions on entanglement distribution and the trade-off of entanglement among the subsystems. Similar results are also derived for negativity.
Relating Two Dialects of Answer Set Programming

Abstract The input language of the answer set solver clingo is based on the definition of a stable model proposed by Paolo Ferraris. The semantics of the ASP-Core language, developed by the ASP Standardization Working Group, uses the approach to stable models due to Wolfgang Faber, Nicola Leone, and Gerald Pfeifer. The two languages are based on different versions of the stable model semantics, and the ASP-Core document requires, “for the sake of an uncontroversial semantics,” that programs avoid the use of recursion through aggregates. In this paper we prove that the absence of recursion through aggregates does indeed guarantee the equivalence between the two versions of the stable model semantics, and show how that requirement can be relaxed without violating the equivalence property.
Satellite Image Enhancement for Small Particle Observation Using Decorrelation Stretcher

In this research work, we have improved the image visual appearance using image decorrelation stretcher techniques. Landsat 8 images are used in this research work to show the improved visual presentation for the images. Landsat 8 images contain 11 different spectrum bands. The visual appearance of the images produces some specific details in terms of appearance, which were not present in the original image. Through the image decorrelation stretcher technique, the image quality is improved and enhanced.
Two entanglement measures

Abstract In a previous work we introduced an entanglement number e ( ψ ) for a vector state ψ . This number relied on the Schmidt decomposition of ψ which may be difficult to compute. We now present a method for finding e ( ψ ) that is simple and efficient. We show that e ( ψ ) is continuous in the vector norm. We next extend e ( ψ ) to an entanglement number e ( ρ ) for a general mixed state ρ and show that ρ is separable (not entangled) if and only if e ( ρ ) = 0. We next define a related quantity e s ( ρ ) ≥ e ( ρ ) which we call the spectral entanglement number. We argue that e s ( ρ ) is easier to compute than e ( ρ ) and that the physical motivation for e s ( ρ ) is superior to that of e ( ρ ). It is shown that e s ( ρ ) is continuous in the operator norm. Although e ( ρ ) is a convex function, we show that e s ( ρ ) need not be convex. An open problem is to characterize the states ρ such that e ( ρ ) = e s ( ρ ). Many illustrative examples are presented.
Integrating consortium blockchain into edge server to defense against ransomware attack

An edge server acts as a data gateway in an IoT network between IoT devices and backend servers. If the edge server is under the ransomware attack, the server’s operation would be interfered by locking its critical files, leading to a single point of failure in the IoT network. This paper proposes a blockchain solution, called Blockchain-enabled Recovery Service (BRS), to tackle the malware injection attack in edge servers. In BRS, edge servers are implemented by nodes in a blockchain network which allows IoT data distributed in edge servers over multiple data connections. It ensures data availability and consistency while improving the network throughput. A simulation test has been conducted to evaluate the performance of edge servers in the blockchain network. The result showed that the size of data record in a ledger is crucial when maximizing the efficiency of a ledger and minimizing data loss in ransomware attack are considered. Moreover, even though the blockchain network is under the attack, data availability and data recovery can be achieved.
Efficient Multi-Class Out-of-Distribution Reasoning for Perception Based Networks: Work-in-Progress

Perception-based deep neural networks used in Cyber Physical Systems are known to fail when faced with inputs that are out-of-distribution (ODD). ODD detection is a complex problem as we need to first identify the shift in the test data from the training distribution and then we need to isolate the responsible generative factor(s) (weather, lighting levels, traffic density, etc.), Unlike the state of the art that uses multi-chained one-class classifiers, we propose an efficient single monitor that uses the principle of disentanglement to train the latent space of a variational autoencoder to be sensitive to distribution shifts in different generative factors. We demonstrate our approach using an end-to-end driving controller in the CARLA simulator.
Food Network System Secured with Blockchain

There are several instances when the Centers for Disease Control and Prevention (CDC) in the USA warns us of some outbreak in some specific or multiple food products. With such warnings, a lot of food gets wasted. However, with proper labeling and trackback system, only the food that is affected could be identified. By doing that, destroying only that food could save a lot of wastage of food. Blockchain is a technique that can trackback, from which farm the bad food is coming, could be identified very easily. The main motive of this research paper is to implement blockchain technology for the food network industry to allow a more secure, universal tracking scheme for shipments and transactions. By comparing this method to current food supply chain management systems, it is concluded that our proposed technique is much efficient and secure in solving the currently existing problems in the food supply chain industry.