Reviewerly/exHarmony huggingface dataset:
Fall Detection Systems at Night

Daytime-based fall detection systems (FADEs) are designed to prevent falls and help individuals carry out their activities independently. However, FADE for nighttime use is still a challenge for researchers. This article reviews various FADE systems and categorizes the open challenges for the design of nighttime-based FADE.
Bottom-up visions for future of food growing in cities

We report on community food growing as an instance of practice-based sustainability research focused on the design of interactive systems for food growing in future cities. We present a case study with a series of workshops using speculative and participatory design approaches focused on creatively exploring futures of urban food growing with a local neighbourhood community. Working with local grassroots communities is often perceived as more egalitarian for promoting viable long-term and embedded change in cities, yet little work has studied this approach for urban food growing. To explore how we might better articulate and conceptualize collaborative food growing futures, we discuss the creation of bottom-up visions as contestations to hegemonic narratives of power and control in cities. These are affected by, limitations of present resources and infrastructures, inability to work at scale due to lack of buy-in of stakeholders, and erroneous promises of future technologies. Through these reflections on grassroots futures as complex assemblages of social and material realities, we provoke researchers and practitioners to look at envisioning future possibilities with participants, as a web of practices and stakeholders. We further suggest that researchers and practitioners explore these interconnections through assemblages of socio-material realities and visions of high- and low-tech futures. This work is important because it provides a new approach to looking at the design of future technologies for cities and addressing systemic issues of hegemonic food systems through bottom-up actionable futures.
Security Issues Associated with Bring Your Own Device (BYOD): A Narrative Review

This study is a narrative review that aimed at exploring the benefits, risks, and policy controls of Bring Your Own Device (BYOD) for maximum and effective BYOD adoption. BYOD is a paradigm where employees use their personal devices such as Smartphones, tablets, laptops, etc., to access organizations’ enterprise resources such as email, file servers, and database contents and networks. Significant empirical evidence from literature revealed that 82% of organizations have a BYOD program, while 68% of organizations see a jump in productivity after switching to BYOD. Despite these important virtues, numerous investigations have shown that threats and attacks on organization data and resources: confidentiality, integrity, and availability are the greatest security concern in BYOD environment. In this study, the authors adopted a narrative review methodology. A search, using keywords, was conducted and fifty-five peer-reviewed journal papers within the last 5 years of this study were identified. These journals came from Pro Quest databases, ScienceDirect, Taylor and Francis, Crossref.com, Walden University collection of scholarly and peer-reviewed journals, and other related texts. Inclusion and exclusion criteria were applied, followed by a quality appraisal of the selected articles, before data were extracted. Results show that good BYOD policies, guidelines, and practices coupled with good controls and management may avert security breaches on employees' and organizations' devices and resources. Results also show that it is possible to use machine learning approaches to handle threats and attacks such as persistent threats, data leakage, and data theft, denial of service (DOS) attack, intrusion attack, and untrusted network in BYOD environment. Findings may positively impact and equip organizations and employees with proven information for successful security and risk management programs that may increase mutual trust and a healthy BYOD adoption environment.
Crime Forecasting using Interpretable Regression Techniques

Over the past years there has been an increase in crimes like theft and burglary, however, this is not evenly distributed because most criminals repeatedly commit crime in the same area until they are arrested. The Pareto principle or the principle of factor sparsity, helps to explain why so many crimes happen in specific places. Fortunately, the field of electronics and informatics has seen a significant advancement in recent times, particularly in the area of artificial intelligence (AI) and its applications. One such application is the use of AI in crime forecasting and analysis, which has the potential to facilitate smart living and smart city initiatives, as well as economic development. This paper presents a study on the use of regression techniques and predictive models, Linear regression, LASSO regression and ridge regression for crime prediction and magnitude estimation, with an average predictive accuracy of 94.0%. The most important features contributing to crime prediction were identified and analyzed using heatmaps thus providing insights for proactive action by crime prevention authorities. Additionally, Local Interpretable Model-agnostic Explanations (LIME) and SHapley Additive explanations (SHAP) were provided to enhance the interpretability and accountability of the developed models. The results of this study have very positive potential implications for smart living and smart city initiatives for developing economies.
Why we need a gradient approach to word order

Abstract This article argues for a gradient approach to word order, which treats word order preferences, both within and across languages, as a continuous variable. Word order variability should be regarded as a basic assumption, rather than as something exceptional. Although this approach follows naturally from the emergentist usage-based view of language, we argue that it can be beneficial for all frameworks and linguistic domains, including language acquisition, processing, typology, language contact, language evolution and change, and formal approaches. Gradient approaches have been very fruitful in some domains, such as language processing, but their potential is not fully realized yet. This may be due to practical reasons. We discuss the most pressing methodological challenges in corpus-based and experimental research of word order and propose some practical solutions.
Cross-Modality Continuous User Authentication and Device Pairing With Respiratory Patterns

At-home screening systems for obstructive sleep apnea (OSA) can bring convenience to remote chronic disease management. However, the unsupervised home environment is subject to spoofing and unintentional interference from the household member. To improve robustness, this work presents SIENNA, an insider-resistant breathing-based authentication/pairing protocol. SIENNA leverages the uniqueness of breathing patterns to automatically and continuously authenticate a user and pairs a mobile OSA app and a physiological monitoring radar system (PRMS). SIENNA does not require biometric enrollment and instead transforms the respiratory measurements taken during the users routine physical checkup into breathing biometrics comparable with the PRMS readings. Furthermore, it can operate within a noisy multi-target home environment and is secure against a co-located attacker through the usage of JADE-ICA, fuzzy commitment, and friendly jamming. We fully implemented SIENNA and evaluated its performance with medium-scale trials. Results show that SIENNA can achieve reliable (> 90% success rate) user authentication and secure device pairing in a noisy environment against an attacker with full knowledge of the authorized users breathing biometrics.
VPP: Privacy Preserving Machine Learning via Undervolting

Machine Learning (ML) systems are susceptible to membership inference attacks (MIAs), which leak private information from the training data. Specifically, MIAs are able to infer whether a target sample has been used in the training data of a given model. Such privacy breaching concern motivated several defenses against MIAs. However, most of the state-of-theart defenses such as Differential Privacy (DP) come at the cost of lower utility (i.e, classification accuracy). In this work, we propose Privacy Preserving Volt $(V_{PP})$, a new lightweight inference-time approach that leverages undervolting for privacy-preserving ML. Unlike related work, V <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PP</inf> maintains protected models’ utility without requiring re-training. The key insight of our method is to blur the MIA differential analysis outcome by comprehensively garbling the model features using random noise. Unlike DP, which injects noise within the gradient at training time, V <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PP</inf> injects computational randomness in a set of layers’ during inference through carefully designed undervolting Specifically, we propose a bi-objective optimization approach to identify the noise characteristics that yield privacypreserving properties while maintaining the protected model’s utility. Extensive experimental results demonstrate that V <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PP</inf> yields a significantly more interesting utility/privacy tradeoff compared to prior defenses. For example, with comparable privacy protection on CIFAR-10 benchmark, V <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PP</inf> improves the utility by 32.93% over DP-SGD. Besides, while related noisebased defenses are defeated by label-only attacks, V <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PP</inf> shows high resilience to such adaptive MLA. More over, V <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PP</inf> comes with a by-product inference power gain of up to 61%. Finally, for a comprehensive analysis, we propose a new adaptive attacks that operate on the expectation over the stochastic model behavior. We believe that V <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PP</inf> represents a significant step towards practical privacy preserving techniques and considerably improves the state-of-the-art.
Statistical ensembles for phase coexistence states specified by noncommutative additive observables

Abstract A phase coexistence state cannot be specified uniquely by any intensive parameters, such as the temperature and the magnetic field, because they take the same values over all coexisting phases. It can be specified uniquely only by an appropriate set of additive observables. Hence, to analyze phase coexistence states the statistical ensembles that are specified by additive observables have been employed, such as the microcanonical and restricted ensembles. However, such ensembles are ill-defined or ill-behaved when some of the additive observables do not commute with each other. Here, we solve this fundamental problem by extending a generalized ensemble in such a way that it is applicable to phase coexistence states that are specified by noncommutative additive observables. We prove that this ensemble correctly gives the density matrix corresponding to phase coexistence states of general quantum systems as well as the thermodynamic functions. Furthermore, these ensembles are convenient for practical calculations because of their good analytic properties and useful formulas by which temperature and other intensive parameters are directly obtained from the expectation values of the additive observables. As a demonstration, we apply our formulation to a two-dimensional system whose phase coexistence states are specified by an additive observable (order parameter) that does not commute with the Hamiltonian.
A Query-By-Example Approach to Compose SPARQL Queries in the GF Framework for Ontology-Based Data Access

The gap between legacy data sources and semantic web technologies can be bridged with the Ontology-Based Data Access (OBDA) methodology by providing protocols and tools for translating old data into ontologies. Querying modern ontologies in OWL/RDF files and understood as networks of objects having describing properties and interlinked by relations requires writing SPARQL queries, where it is essential to have a technical proficiency not usually in the hands of lay users. For studying this problem, we developed a prototype tool called GF to capable of executing arbitrary SPARQL queries posed against OWL/RDF ontologies obtained by OBDA from H2 relational databases as well as Excel and CSV spreadsheets. The tool allows to visually specify a subset of SPARQL selection and construction queries using a Query-By-Example approach. Our research shows that, given an OWL/RDF ontology obtained from any suitable data source, our implementation can express an important proper subset of queries and translate them into executable SPARQL queries.
PROVE: Provable remote attestation for public verifiability

The expanding attack surface of Internet of Things (IoT) systems calls for innovative security approaches to verify the reliability of IoT devices. To this end, Remote Attestation (RA) serves as a key mechanism that remotely detects the presence of malware in IoT devices. Typically, RA allows a centralized trusted Verifier to retrieve reliable evidence about the software integrity of an untrusted Prover. Existing RA schemes generally rely on the assumption that the Verifier and the Prover know each other and have pre-shared cryptographic keys during the bootstrap phase. However, these assumptions are not realistic to employ over commonly used event-driven IoT networks, in which the interacting parties do not know each other and do not communicate directly. This paper proposes PROVE, a novel protocol that allows many Verifiers to attest one or more Provers without pre-shared key material and without using public-key cryptography which is often not suitable for resource-constraint IoT devices. In particular, PROVE considers a realistic IoT system where devices adopt the publish/subscribe communication paradigm. In PROVE, the subscribers act as untrusted Verifiers and attest not only the firmware integrity of the publishers that act as untrusted Provers but also the authenticity of the received data originated from these publishers. We simulate PROVE on the Contiki emulator and demonstrate the scalability of the solution. We also validate PROVE through two hardware proof-of-concept implementations: PROVE and PROVE+, which rely on different cryptographic cores. The results show that a complete execution of the protocol takes 4605 ns and 324 ns for PROVE and PROVE+, respectively.
Distribution of Bipartite and Tripartite Entanglement within a Spin-1/2 Heisenberg Star in a Magnetic Field

The spatial distribution of entanglement within a spin-1/2 Heisenberg star composed from a single central spin and three peripheral spins is examined in the presence of an external magnetic field using the Kambe projection method, which allows an exact calculation of the bipartite and tripartite negativity serving as a measure of the bipartite and tripartite entanglement. Apart from a fully separable polarized ground state emergent at high-enough magnetic fields, the spin-1/2 Heisenberg star exhibits at lower magnetic fields three outstanding nonseparable ground states. The first quantum ground state exhibits the bipartite and tripartite entanglement over all possible decompositions of the spin star into any pair or triad of spins, whereby the bipartite and tripartite entanglement between the central and peripheral spins dominates over that between the peripheral spins. The second quantum ground state has a remarkably strong tripartite entanglement between any triad of spins in spite of the lack of bipartite entanglement. The central spin of the spin star is separable from the remaining three peripheral spins within the third quantum ground state, where the peripheral spins are subject to the strongest tripartite entanglement arising from a two-fold degenerate W-state.
ANALISIS KLASTER KEMISKINAN KABUPATEN KOTA DI PROVINSI BANTEN MENGGUNAKAN METODE K-MEANS

Poverty is a special problem that needs to be discussed in Indonesia. Because poverty that occurs in a certain area will have an impact on development in Indonesia. Banten province has a percentage of 6.04 percent with a total poverty of 576.62 thousand poor people. Given this, the Provincial Government of Banten requires knowledge of district/city socio-economic data in Banten Province. This study aims to cluster regencies/cities in Banten Province using the K-Means method. The K-Means method is a clustering technique that aims to group data based on the same characteristics. The variables observed in this study were the percentage of poor people aged 15 and over who had graduated from elementary/junior high school, the percentage of poor people aged 15 and over who had finished high school and above, the percentage of poor people aged 15 and over by district/city not working, the percentage of poor people aged 15 Years and over by district/city working in the informal sector, percentage of poor population aged 15 and over by district/city working in the formal sector, percentage of expenditure per capita for food by district/city and poor status. the results of the analysis showed that the district/city poverty clustering in Banten Province using the K-means method obtained 2 clusters, namely cluster 1 consisting of Tangerang City, Cilegon City and South Tangerang City and cluster 2 consisting of Pandeglang Regency, Lebak Regency, Tangerang, Serang Regency, Serang City. And formation 2 cluster has a good structural test value based on the results of the silhouette index so that 2 cluster has the best accuracy value compared to 3 clusters and 4 cluster.
Optimized model analysis of blockchain PoW procotol under long delay attack

Abstract:Proof of work(POW) is one of the most widely used consensus method of bitcoin. In some chains, because of the large number of users, the huge amount of information interaction data, equipment hardware failure or malicious attacks on some nodes may cause communication delay, All of these may engender forks on the blockchain resulting data lose. Therefore, whether the blockchain protocol can achieve sufficient security in the asynchronous network environment with long delay, so how to reduce fork and user's data loss caused by long delay attack are important issue related to blockchain protocol. In this study,we optimized the existing model and proposed TOD to describe the evolution state of the main chain accurately.
Deep Graph Stream SVDD: Anomaly Detection in Cyber-Physical Systems

Our work focuses on anomaly detection in cyber-physical systems. Prior literature has three limitations: (1) Failing to capture long-delayed patterns in system anomalies; (2) Ignoring dynamic changes in sensor connections; (3) The curse of high-dimensional data samples. These limit the detection performance and usefulness of existing works. To address them, we propose a new approach called deep graph stream support vector data description (SVDD) for anomaly detection. Specifically, we first use a transformer to preserve both short and long temporal patterns of monitoring data in temporal embeddings. Then we cluster these embeddings according to sensor type and utilize them to estimate the change in connectivity between various sensors to construct a new weighted graph. The temporal embeddings are mapped to the new graph as node attributes to form weighted attributed graph. We input the graph into a variational graph auto-encoder model to learn final spatio-temporal representation. Finally, we learn a hypersphere that encompasses normal embeddings and predict the system status by calculating the distances between the hypersphere and data samples. Extensive experiments validate the superiority of our model, which improves F1-score by 35.87%, AUC by 19.32%, while being 32 times faster than the best baseline at training and inference.
FEATURES OF FORMATIVE ASSESSMENT OF STUDENTS' EDUCATIONAL ACHIEVEMENTS IN ONLINE LESSONS

Reforms in education involve changes in approaches to the organization of the educational process, evaluation of the results of students' educational activities. The methodological recommendations of the Ministry of Education and Culture specify the types of assessment that teachers should plan and implement in educational activities. Among them are current, final and formative. The latter differs from the previous ones in that it actively involves students in evaluating their own initial results, provides support, is positively oriented, and is constantly implemented in class. Modern conditions in Ukraine create challenges for teachers regarding the introduction of formative assessment in online learning. On the basis of international and domestic research and own practice, the authors have determined and adapted an assessment strategy for the online lesson, for each of which web-based formative assessment tools are offered. The article presents a description of the planning of evaluation activities, substantiates the possibility of establishing cooperative learning, and specifies the mechanisms for evaluating the results and process of group work. Special attention is paid to the involvement of students in the self-evaluation of the learning process and their educational achievements. A set of pedagogical actions for the formation of adequate student self-esteem is provided. The identified online tools allow you to implement all assessment strategies and assess students in a variety of ways. At the same time, they optimize the evaluation activity, which is very important for an online lesson, visualize it and provide quick feedback to all subjects of the educational process. The authors see the development of a training course for teachers on the introduction of formative assessment of students' educational achievements in online lessons as a promising direction for further research.
AlphaZe∗∗: AlphaZero-like baselines for imperfect information games are surprisingly strong

In recent years, deep neural networks for strategy games have made significant progress. AlphaZero-like frameworks which combine Monte-Carlo tree search with reinforcement learning have been successfully applied to numerous games with perfect information. However, they have not been developed for domains where uncertainty and unknowns abound, and are therefore often considered unsuitable due to imperfect observations. Here, we challenge this view and argue that they are a viable alternative for games with imperfect information—a domain currently dominated by heuristic approaches or methods explicitly designed for hidden information, such as oracle-based techniques. To this end, we introduce a novel algorithm based solely on reinforcement learning, called AlphaZe∗∗, which is an AlphaZero-based framework for games with imperfect information. We examine its learning convergence on the games Stratego and DarkHex and show that it is a surprisingly strong baseline, while using a model-based approach: it achieves similar win rates against other Stratego bots like Pipeline Policy Space Response Oracle (P2SRO), while not winning in direct comparison against P2SRO or reaching the much stronger numbers of DeepNash. Compared to heuristics and oracle-based approaches, AlphaZe∗∗ can easily deal with rule changes, e.g., when more information than usual is given, and drastically outperforms other approaches in this respect.