Adamakopoulos and Others v. Cyprus: “Massive” Problems Concerning a Mass Claims Proceeding in Investment Treaty Arbitration?

The Adamakopoulos and Others v. Cyprus Decision is noteworthy because it provides a blueprint for mass claims proceedings in investment treaty arbitration, justifying the possibility of addressing investment claims en masse in the future. This case comment reviews the background to the dispute, addresses the majority decision on the mass claims, and comments on the Tribunal's reasoning regarding the non-requirement of additional consent to mass claims arbitration, the homogeneity of the claims, and procedural flexibility.

Majority Decision Aggregation with Binarized Data in Wireless Sensor Networks

Wireless sensor networks (WSNs) are the cornerstone of the current Internet of Things era. They have limited resources and features, a smaller packet size than other types of networks, and dynamic multi-hop transmission. WSNs can monitor a particular area of interest and are used in many different applications. For example, during the COVID-19 pandemic, WSNs have been used to measure social distancing/contact tracing among people. However, the major challenge faced by WSN protocols is limited battery energy. Therefore, the whole WSN area is divided into odd clusters using k-means++ clustering to make a majority rule decision to reduce the amount of additional data sent to the base station (or sink) and achieve node energy-saving efficiency. This study proposes an energy-efficient binarized data aggregation (EEBDA) scheme, by which, through a threshold value, the collected sensing data are asserted with binary values. Subsequently, the corresponding cluster head (CH), according to the Hamming weight and the final majority decision, is calculated and sent to the base station (BS). The EEBDA is based on each cluster and divides the entire WSN area into four quadrants. All CHs construct a data-relay transmission link in the same quadrant; the binary value is transferred from the CHs to the sink. The EEBDA adopts a CH rotation scheme to aggregate the data based on the majority results in the cluster. The simulation results demonstrate that the EEBDA can reduce redundant data transmissions, average the energy consumption of nodes in the cluster, and obtain a better network lifetime when compared to the LEACH, LEACH-C, and DEEC algorithms.

Majority Decision-Making Works Best Under Conditions of Leadership Ambiguity and Shared Task Representations

The effectiveness of decision-making teams depends largely on their ability to integrate and make sense of information. Consequently, teams which more often use majority decision-making may make better quality decisions, but particularly so when they also have task representations which emphasize the elaboration of information relevant to the decision, in the absence of clear leadership. In the present study we propose that (a) majority decision-making will be more effective when task representations are shared, and that (b) this positive effect will be more pronounced when leadership ambiguity (i.e., team members’ perceptions of the absence of a clear leader) is high. These hypotheses were put to the test using a sample comprising 81 teams competing in a complex business simulation for seven weeks. As predicted, majority decision-making was more effective when task representations were shared, and this positive effect was more pronounced when there was leadership ambiguity. The findings extend and nuance earlier research on decision rules, the role of shared task representations, and leadership clarity.

Constitutional Law and Islamic Hijabs in Kenyan Schools: The Supreme Court’s Specious Constraint in Methodist Church in Kenya

ABSTRACT This article analyses the content and implications of the Supreme Court of Kenya’s judgment in Methodist Church in Kenya v Mohamed Fugicha and 3 Others. There, by majority decision, the Supreme Court overturned the Court of Appeal’s ruling that reasonable accommodation be made for the wearing of Islamic hijabs by female Muslim students in Kenyan schools. While Methodist Church in Kenya was expected to clarify the scope of the right to manifest religious belief in Kenya, the Supreme Court used specious logic based on legalism to avoid that issue. This article shows how the majority decision contradicts principles of enforcement of constitutional rights by focusing unduly on procedural technicalities, avoiding the core issue of permissible restrictions on religious expression and leaving key legal questions unresolved. It also highlights the well-reasoned dissenting opinion that addressed the core issue and which has crucial import for future development of religious freedom jurisprudence in Kenya

Speech emotion recognition based on listener-dependent emotion perception models

This paper presents a novel speech emotion recognition scheme that leverages the individuality of emotion perception. Most conventional methods simply poll multiple listeners and directly model the majority decision as the perceived emotion. However, emotion perception varies with the listener, which forces the conventional methods with their single models to create complex mixtures of emotion perception criteria. In order to mitigate this problem, we propose a majority-voted emotion recognition framework that constructs listener-dependent (LD) emotion recognition models. The LD model can estimate not only listener-wise perceived emotion, but also majority decision by averaging the outputs of the multiple LD models. Three LD models, fine-tuning, auxiliary input, and sub-layer weighting, are introduced, all of which are inspired by successful domain-adaptation frameworks in various speech processing tasks. Experiments on two emotional speech datasets demonstrate that the proposed approach outperforms the conventional emotion recognition frameworks in not only majority-voted but also listener-wise perceived emotion recognition.

Classification of glomerular pathological findings using deep learning and nephrologist–AI collective intelligence approach

BackgroundAutomated classification of glomerular pathological findings is potentially beneficial in establishing an efficient and objective diagnosis in renal pathology. While previous studies have verified the artificial intelligence (AI) models for the classification of global sclerosis and glomerular cell proliferation, there are several other glomerular pathological findings required for diagnosis, and the comprehensive models for the classification of these major findings have not yet been reported. Whether the cooperation between these AI models and clinicians improves diagnostic performance also remains unknown. Here, we developed AI models to classify glomerular images for major findings required for pathological diagnosis and investigated whether those models could improve the diagnostic performance of nephrologists.MethodsWe used a dataset of 283 kidney biopsy cases comprising 15888 glomerular images that were annotated by a total of 25 nephrologists. AI models to classify seven pathological findings: global sclerosis, segmental sclerosis, endocapillary proliferation, mesangial matrix accumulation, mesangial cell proliferation, crescent, and basement membrane structural changes, were constructed using deep learning by fine-tuning of InceptionV3 convolutional neural network. Subsequently, we compared the agreement to truth labels between majority decision among nephrologists with or without the AI model as a voter.ResultsOur model for global sclerosis showed high performance (area under the curve: periodic acid-Schiff, 0.986; periodic acid methenamine silver, 0.983); the models for the other findings also showed performance close to those of nephrologists. By adding the AI model output to majority decision among nephrologists, the sensitivity and specificity were significantly improved in 9 of 14 constructed models compared to those of nephrologists alone.ConclusionOur study showed a proof-of-concept for the classification of multiple glomerular findings in a comprehensive method of deep learning and suggested its potential effectiveness in improving diagnostic accuracy of clinicians.

Ensuring the dependability of technical facilities through triplication and quadrupling

Redundancy, e.g. structural redundancy, is one of the primary methods of improving the dependability, ensures failsafety and fault tolerance of components, devices and systems. According to the International Patent Classification (IPC), the class of systems and methods G06F11/18 is defined as «using passive fault-masking of the redundant circuits, e.g. by quadrupling or by majority decision circuits». Obviously, «fault-masking» masks not only faults, but failures as well. The majority decision circuits (MDC) in the minimal configuration implements a «2-out-of-3» choice. According to the above definition, such redundancy should not require a special decision circuit. However, that is not always the case. In cases when the resulting signal out of a quadruple logic is delivered to, for instance, an executive device, a «3-outof-4» selection circuit is required anyway. Another dependability-improving solution is defined by class G06F 11/20, «using active fault-masking, e.g. by switching out faulty elements or by switching in spare elements». The word «active» is missing here, thus we have active and passive fault tolerance. The paper examines passive fault tolerance that uses triplication and quadrupling and compares the respective probabilities of no-failure.The Weibull distribution is used that most adequately describes dependability in terms of radiation durability under the effects of heavy ions. It shows that in a number of cases quadrupling has a lower redundancy than triplication. A formula is proposed that describes the conditions of preferability of quadrupling at transistor level.