Reviewerly/exHarmony huggingface dataset:
Design and implementation of a solar integration in electric smart bench

Abstract Energy problems that hit the world have implications for savings in all fields, including lighting public spaces. However, lighting conventional public areas is an inappropriate step because the monthly electricity costs required are already very expensive. This paper proposes new solutions for alternative sources of electrical energy in open spaces. We validate this solution by implementing a solar cell in a park bench object. The experimental results show that the proposed solution has the same performance as conventional lighting. Electric park benches can operate longer by using a solar cell with an output power of 26.76 Watt-peak hours. With the enormous potential of solar power, the application of solar cells in an open environment is very suitable. With this electric bench, it is still energy efficient and green energy.
From Artificial Intelligence to Dissipative Sociotechnical Rationality: Cyberphysical and Sociocultural Matrices of the Digital Age

Artificial-intelligence and converging technologies currently setting global post-industrial (economic, cultural, social, and manufacturing) trends, such as the Internet of things, penetrating computing, physical-digital communication, smart medicine and smart education replacing step by step the traditional forms of communications, logistics, management, data logging, and automation.
Hidden semi-Markov-switching quantile regression for time series

A hidden semi-Markov-switching quantile regression model is introduced as an extension of the hidden Markov-switching one. The proposed model allows for arbitrary sojourn-time distributions in the states of the Markov-switching chain. Parameters estimation is carried out via maximum likelihood estimation method using the Asymmetric Laplace distribution. As a by product of the model specification, the formulae and methods for forecasting, the state prediction, decoding and model checking that exist for ordinary hidden Markov-switching models can be applied to the proposed model. A simulation study to investigate the behaviour of the proposed model is performed covering several experimental settings. The empirical analysis studies the relationship between the stock index from the emerging market of China and those from the advanced markets, and investigates the determinants of high levels of pollution in an Italian small city.
Code Structures for Quantum Encryption and Decryption

The paradigm of quantum computation has led to the development of new algorithms as well variations on existing algorithms. In particular, novel cryptographic techniques based upon quantum computation are of great interest. Many classical encryption techniques naturally translate into the quantum paradigm because of their well-structured factorizations and the fact that they can be phased in the form of unitary operators. In this work, we demonstrate a quantum approach to data encryption and decryption based upon the McEliece cryptosystem using Reed-Muller codes. This example is of particular interest given that post-quantum analyses have highlighted this system as being robust against quantum attacks. Finally, in anticipation of quantum computation operating over binary fields, we discuss alternative operator factorizations for the proposed cryptosystem.
1.3 Working at the Intersection of Machine Learning, Signal Processing, Sensors, and Circuits

Wireless technologies have propelled major advances in various industries including telecommunications, entertainment, and smart environments. Today, radios are embedded in cellphones, wearable devices, sensors, and control systems. Yet, in all of these diverse domains, radios are used for communications. Even when embedded in a sensor, the radio signals are used for communicating the sensed data, rather than performing the sensing function. We argue that future innovations should leverage radio signals for sensing. Radio signals are fascinating creatures: they travel in space, traverse walls and occlusions, and reflect off people and objects. The reflected signals are modulated by people's movements, actions, respiration, and heart beats. By analyzing such radio reflections, we can learn much information about people and their physiological signals, and leverage such knowledge to enable new capabilities and novel applications.
Solving the Minimum Spanning Tree Problem with a Quantum Annealer

Quantum annealing (QA) is a different technology from gate-model quantum computing. This research proposes a novel technique for solving the Minimum Spanning Tree (MST) (or, the minimum weight spanning tree) problem on a quantum annealer. The problem is of interest due to its applications in clustering, unsupervised learning, network design, and image processing to name a few. The advent of quantum cloud computing has provided access to quantum computing tools, previously unavailable to the general community. D-Wave systems recently released cloud access to their quantum annealer type hardware, which this project leverages to provide a novel solution method to the MST problem.
Passing Through Narrow Gaps with Deep Reinforcement Learning

The DARPA subterranean challenge requires teams of robots to traverse difficult and diverse underground environments. Traversing small gaps is one of the challenging scenarios that robots encounter. Imperfect sensor information makes it difficult for classical navigation methods, where behaviours require significant manual fine tuning. In this paper we present a deep reinforcement learning method for autonomously navigating through small gaps, where contact between the robot and the gap may be required. We first learn a gap behaviour policy to get through small gaps (only centimeters wider than the robot). We then learn a goal-conditioned behaviour selection policy that determines when to activate the gap behaviour policy. We train our policies in simulation and demonstrate their effectiveness with a large tracked robot in simulation and on the real platform. In simulation experiments, our approach achieves 93% success rate when the gap behaviour is activated manually by an operator, and 63% with autonomous activation using the behaviour selection policy. In real robot experiments, our approach achieves a success rate of 73% with manual activation, and 40% with autonomous behaviour selection. While we show the feasibility of our approach in simulation, the difference in performance between simulated and real world scenarios highlight the difficulty of direct sim-to-real transfer for deep reinforcement learning policies. In both the simulated and real world environments alternative methods were unable to traverse the gap.
Finance, Institutions, and Private Investment in Africa

Abstract This article extends the debate on finance versus institutions and measurement of property rights institutions (PRI). We assess the relationships between various components of PRI and private investment, notably: political, economic, and institutional governances. Comparative concurrent relationships of financial dynamics of depth, efficiency, activity, and size are also investigated. The findings provide support for the quality of institutions as a better positive correlate of private investment than financial intermediary development. The interaction of finance and governance is not significant in potentially promoting private investment, perhaps due to substantially documented surplus liquidity issues in African financial institutions. The empirical evidence is based on 53 African countries for the period 1996‐2010. Policy measures are discussed for reducing financial deposits, increasing financial activity, and hence, improving financial efficiency. Related Articles Aideyan, Osaore. 2016. “Political and Institutional Prerequisites for Monetary Union: Assessing Progress in the Economic Community of West African States (ECOWAS).” Politics &amp; Policy 44 (6): 1192‐1212. https://doi.org/10.1111/polp.12183 Asongu, Simplice A., and Joseph Nnanna. 2019. “Foreign aid, Instability, and Governance in Africa.” Politics &amp; Policy 47 (4): 807‐848. https://doi.org/10.1111/polp.12320 Scarlato, Margherita, and Giorgio d'Agostino. 2019. “The Political Dimension of Cash Transfers in Latin America and Sub‐Saharan Africa: A Comparative Perspective.” Politics &amp; Policy 47 (6): 1125‐1155. https://doi.org/10.1111/polp.12332
Learning Human Rewards by Inferring Their Latent Intelligence Levels in Multi-Agent Games: A Theory-of-Mind Approach with Application to Driving Data

Reward function, as an incentive representation that recognizes humans’ agency and rationalizes humans’ actions, is particularly appealing for modeling human behavior in human-robot interaction. Inverse Reinforcement Learning is an effective way to retrieve reward functions from demonstrations. However, it has always been challenging when applying it to multi-agent settings since the mutual influence between agents has to be appropriately modeled. To tackle this challenge, previous work either exploits equilibrium solution concepts by assuming humans as perfectly rational optimizers with unbounded intelligence or pre-assigns humans’ interaction strategies a priori. In this work, we advocate that humans are bounded rational and have different intelligence levels when reasoning about others’ decision-making process, and such an inherent and latent characteristic should be accounted for in reward learning algorithms. Hence, we exploit such insights from Theory-of-Mind and propose a new multi-agent Inverse Reinforcement Learning framework that reasons about humans’ latent intelligence levels during learning. We validate our approach in both zero-sum and general-sum games with synthetic agents, and illustrate a practical application to learning human drivers’ reward functions from real driving data. We compare our approach with two baseline algorithms. The results show that by reasoning about humans’ latent intelligence levels, the proposed approach has more flexibility and capability to retrieve reward functions that explain humans’ driving behaviors better.
Access to educational information enabled by ICT tools in the Fort Beaufort Education District (FBED), Eastern Cape, South Africa

The benefits of the application of ICT tools in the education of high school learners have been well-established in various studies. As developments in technology have increased the reach of electronic information resources and enhanced learners’ access to educational information, it is imperative that these tools be available and accessible without any hindrances whatsoever to high school learners. This study was set out to evaluate the state of ICT infrastructure available to high school learners for the advancement of access to educational information while accentuating the efforts by the South African government to promote computer access in schools. This study underscores the importance of ICT tools for high school learners in ensuring quality educational outcome. A triangulated design was adopted to collect both qualitative and quantitative data on the condition of ICT-enabled access to educational information from Grade 12 learners and teachers in the Fort Beaufort Education District, Eastern Cape South Africa. Results show that learners’ access to educational information is constrained by inadequate provision of ICT infrastructure, restricted access to ICT tools, regulations for use of personal ICT tools and absence of information literacy for high school learners. The study, therefore, recommends the improved and dedicated provision of ICT resources, ICT-enabled educational information literacy program and flexibility of rules regarding the use of personal ICT tools at school.
Maximal Instance Algorithm for Fast Mining of Spatial Co-Location Patterns

The explosive growth of spatial data and the widespread use of spatial databases emphasize the need for spatial data mining. The subsets of features frequently located together in a geographic space are called spatial co-location patterns. It is difficult to discover co-location patterns because of the huge amount of data brought by the instances of spatial features. A large fraction of the computation time is devoted to generating row instances and candidate co-location patterns. This paper makes three main contributions for mining co-location patterns. First, the definition of maximal instances is given and a row instance (RI)-tree is constructed to find maximal instances from a spatial data set. Second, a fast method for generating all row instances and candidate co-locations is proposed and the feasibility of this method is proved. Third, a maximal instance algorithm with no join operations for mining co-location patterns is proposed. Finally, experimental evaluations using synthetic data sets and a real data set show that maximal instance algorithm is feasible and has better performance.
Multiplex Network Embedding Model with High-Order Node Dependence

Multiplex networks have been widely used in information diffusion, social networks, transport, and biology multiomics. They contain multiple types of relations between nodes, in which each type of the relation is intuitively modeled as one layer. In the real world, the formation of a type of relations may only depend on some attribute elements of nodes. Most existing multiplex network embedding methods only focus on intralayer and interlayer structural information while neglecting this dependence between node attributes and the topology of each layer. Attributes that are irrelevant to the network structure could affect the embedding quality of multiplex networks. To address this problem, we propose a novel multiplex network embedding model with high-order node dependence, called HMNE. HMNE simultaneously considers three properties: (1) intralayer high-order proximity of nodes, (2) interlayer dependence in respect of nodes, and (3) the dependence between node attributes and the topology of each layer. In the intralayer embedding phase, we present a symmetric graph convolution-deconvolution model to embed high-order proximity information as the intralayer embedding of nodes in an unsupervised manner. In the interlayer embedding phase, we estimate the local structural complementarity of nodes as an embedding constraint of interlayer dependence. Through these two phases, we can achieve the disentangled representation of node attributes, which can be treated as fined-grained semantic dependence on the topology of each layer. In the restructure phase of node attributes, we perform a linear fusion of attribute disentangled representations for each node as a reconstruction of original attributes. Extensive experiments have been conducted on six real-world networks. The experimental results demonstrate that the proposed model outperforms the state-of-the-art methods in cross-domain link prediction and shared community detection tasks.
MAS3K: An Open Dataset for Marine Animal Segmentation

Recent advances in marine animal research have raised significant demands for fine-grained marine animal segmentation techniques. Deep learning has shown remarkable success in a variety of object segmentation tasks. However, deep based marine animal segmentation is lack of investigation due to the short of a marine animal dataset. To this end, we elaborately construct the first open Marine Animal Segmentation dataset, called MAS3K, which consists of more than three thousand images of diverse marine animals, with common and camouflaged appearances, in different underwater conditions, such as low illumination, turbid water quality, photographic distortion, etc. Each image from the MAS3K dataset has rich annotations, including an object-level annotation, a category name, an animal camouflage method (if applicable), and attribute annotations. In addition, based on MAS3K, we systematically evaluate 6 cutting-edge object segmentation models using five widely-used metrics. We perform comprehensive analysis and report detailed qualitative and quantitative benchmark results in the paper. Our work provides valuable insights into the marine animal segmentation, which will boost the development in this direction effectively.
Object Tracking by the Least Spatiotemporal Searches

Tracking a suspicious car or a person in a city efficiently is crucial in urban safety management. But how can we complete the task with the minimal number of spatiotemporal searches when massive camera records are involved? To this end, this study proposes a strategy named intermediate searching at heuristic moments (IHMs). At each step, we determine which moment is the best one for the search based on a heuristic indicator. Then, at that moment, locations are searched one by one in descending order of predicted appearing probabilities until a search hit is obtained. We iterate this step until we derive the object's current location. Five searching strategies are compared via experiments. Among these strategies, the IHMs strategy is validated as the most efficient. IHMs can save up to 1/3 of the total cost. This result provides evidence that “searching at intermediate moments can save cost”.
An Analysis of Citations in Chinese English-major Master’s Theses and Doctoral Dissertations

This study investigated citation features of Chinese English-major master's theses and doctoral dissertations and compared the citations in the literature reviews and discussions. Xu's (2016) framework based on systemic functional linguistics was expanded to incorporate content, function, writer's stance and types of citations into the ideational, interpersonal and textual dimensions. The literature review and discussion chapters in 30 master's theses and 30 doctoral dissertations were randomly collected for analysis. Results show that doctoral students fulfilled two functions of citations – application and agreement – significantly more than master's students. Doctoral students are better able to evaluate and apply previous models in their research and start to establish their academic identity by presenting direct support. Further analysis indicates that the functions of literature reviews and discussions influence the distribution of citation features. Both groups used one citation function – topic-relevance – more in discussions than in literature reviews to interpret their findings and compare them against previous scholars' claims. This study provides implications for theorizing L2 research writing and offers advice for students to improve their citation competence.
Walmart Sale Forecasting Model Based On LightGBM

Accurate sales forecasts can help businesses make reasonable sales plans. In this paper, I am going to build a model for sales forecasting. First, I need to preprocess the historical data, find the obvious feature structure in the data, and eliminate the unreasonable data. I propose a GBDT-based LightGBM model, the Root Mean Square Error of this model is 2.09, which shows better predictive ability compared with linear regression model and SVM model.
A New Method for Redundancy Analysis in Feature Selection

Feature selection has become an important research issue in the fields of pattern recognition, data mining and machine learning. When processing some high-dimensional data, traditional machine learning algorithms may not be able to get satisfactory results, while feature selection can filter features of high-dimensional data before model training, reduce the number of features, and thus reduce the impact of problems caused by high-dimensional data. Feature selection can simultaneously eliminate features that are less correlated with categories or redundant with selected features, so as to improve classification accuracy and learning and training efficiency of high-dimensional data tasks. However, existing methods may remove redundancy inadequately or excessively in some cases. Therefore, this paper proposes a criterion for the feature redundancy, and based on this criterion, designs an effective feature selection algorithm to remove redundant features on the premise of ensuring maximum relevance to the target variable. The effectiveness and efficiency of the proposed algorithm are verified by experimental comparison with other algorithms that can remove redundant features.
Blindspots in Python and Java APIs Result in Vulnerable Code

Blindspots in APIs can cause software engineers to introduce vulnerabilities, but such blindspots are, unfortunately, common. We study the effect APIs with blindspots have on developers in two languages by replicating a 109-developer, 24-Java-API controlled experiment. Our replication applies to Python and involves 129 new developers and 22 new APIs. We find that using APIs with blindspots statistically significantly reduces the developers’ ability to correctly reason about the APIs in both languages, but that the effect is more pronounced for Python. Interestingly, for Java, the effect increased with complexity of the code relying on the API, whereas for Python, the opposite was true. This suggests that Python developers are less likely to notice potential for vulnerabilities in complex code than in simple code, whereas Java developers are more likely to recognize the extra complexity and apply more care, but are more careless with simple code. Whether the developers considered API uses to be more difficult, less clear, and less familiar did not have an effect on their ability to correctly reason about them. Developers with better long-term memory recall were more likely to correctly reason about APIs with blindspots, but short-term memory, processing speed, episodic memory, and memory span had no effect. Surprisingly, professional experience and expertise did not improve the developers’ ability to reason about APIs with blindspots across both languages, with long-term professionals with many years of experience making mistakes as often as relative novices. Finally, personality traits did not significantly affect the Python developers’ ability to reason about APIs with blindspots, but less extroverted and more open developers were better at reasoning about Java APIs with blindspots. Overall, our findings suggest that blindspots in APIs are a serious problem across languages, and that experience and education alone do not overcome that problem, suggesting that tools are needed to help developers recognize blindspots in APIs as they write code that uses those APIs.
Method of Indirect Steganographic Coding of Information without Visual Distortion of the Video Container series

A method of an indirect steganographic embedding is proposed, which allows to ensure the security of information resources based on the use of structural-combinatorial redundancy of the video container. Within the method, the embedding of information is performed simultaneously in the space-time domain of the video resource, as well as in spectral space and spectral-time space.
Detecting variability: A study on the application of bayesian multilevel modelling to archaeological data. Evidence from the Neolithic Adriatic and the Bronze Age Aegean

The detection and interpretation of variability in archaeological data has been a long-standing effort in the field. This paper aims to introduce the application of Bayesian multilevel modelling as a tool for the detection of variability at levels within nested archaeological data. Model structure, ways of construction, and the potential of using variability information to enhance archaeological interpretations is presented. This is demonstrated through the analysis of two case study datasets: Neolithic pottery finds from Mala (Nova) Pećina cave excavations in Croatia and stone finds from the Bronze Age site of Akrotiri, Thera, Greece. This is followed by a discussion of the multilevel model results and the possible interpretations that can be derived from them. Finally, propositions are made on how these and other models can be extended.